International Journal of Advanced Research and Innovations
Elevating Global Research. Empowering Scientific Innovation. The International Journal of Advanced Research and Innovations (IJARI) is a premier international, peer-reviewed, open-access journal dedicated to advancing scientific excellence, fostering multidisciplinary innovation, and supporting high-impact research across the globe. International Journal of Advanced Research and Innovations (IJARI) provides a trusted publishing environment where scholars, researchers, and practitioners share groundbreaking discoveries that shape the future of science, technology, and society. About International Journal of Advanced Research and Innovations (IJARI) International Journal of Advanced Research and Innovations (IJARI) is committed to publishing original, ethical, and impactful research that contributes meaningfully to global knowledge systems. The journal welcomes high-quality manuscripts across multiple domains, including cutting-edge experimental work, theoretical advancements, applied innovations, interdisciplinary studies, and critical scientific reviews. With a rigorous double-blind peer-review process, International Journal of Advanced Research and Innovations (IJARI) ensures that every article meets the highest standards of academic integrity, credibility, and scientific rigor. As an open-access platform, the journal offers free and immediate access to all published content, enabling broad dissemination and maximum research visibility worldwide. Why Choose International Journal of Advanced Research and Innovations (IJARI)? Global Reach & Maximum Visibility: All articles are freely accessible, ensuring wide readership, increased citations, and strong international impact. High-Quality Peer Review: Manuscripts undergo an unbiased, double-blind peer-review process conducted by expert reviewers from diverse academic disciplines. Fast & Transparent Editorial Processing: International Journal of Advanced Research and Innovations (IJARI) provides an efficient submission workflow, clear communication, and timely editorial decisions without compromising quality. Ethical & Trusted Publishing Standards: The journal adheres to internationally recognized ethical guidelines, ensuring originality, proper citation, and research integrity. Broad Interdisciplinary Scope: IJARI accepts innovative research from a wide range of fields, including: Life & Biological Sciences Physical & Chemical Sciences Engineering & Technology Computer & Information Sciences Medicine & Health Sciences Environmental & Earth Sciences Social & Interdisciplinary Research Emerging Technologies & Applied Innovations Editorial Excellence The International Journal of Advanced Research and Innovations (IJARI) Editorial Board consists of globally recognized scholars, domain specialists, and experienced reviewers dedicated to maintaining the highest standards of scholarly publishing. The editorial team ensures: Fair and objective evaluation Constructive and actionable feedback Strict confidentiality and conflict-free assessments Commitment to transparency and ethical research practices What International Journal of Advanced Research and Innovations (IJARI) Publishes The journal accepts a wide range of scholarly contributions, including: Original Research Articles Review Papers Short Communications Case Studies Technical Notes Perspectives & Commentaries Interdisciplinary and Emerging Research Each manuscript is carefully assessed for originality, significance, methodology, and contribution to the field. Mission and Vision Our Mission: To advance global scientific knowledge by publishing high-quality, accessible, and ethically responsible research that promotes innovation, collaboration, and societal progress. Our Vision: To become a leading international hub for interdisciplinary research, fostering a research culture that values excellence, transparency, and global scientific exchange. Submit Your Manuscript International Journal of Advanced Research and Innovations (IJARI) invites researchers to submit high-quality manuscripts that contribute new knowledge, challenge existing paradigms, or offer meaningful solutions to scientific and societal challenges. Authors benefit from: Easy online submission Transparent manuscript tracking Professional editorial support Wide international readership Share your research with the world. Submit your manuscript to International Journal of Advanced Research and Innovations (IJARI) and be part of a global community of innovation. Join International Journal of Advanced Research and Innovations (IJARI) – Where Research Meets Innovation By choosing International Journal of Advanced Research and Innovations (IJARI), you join an international network of scholars committed to ethical publishing, scientific leadership, and multidisciplinary advancement.
TOWARDS AN INTELLIGENT CANCER LEARNING HEALTH SYSTEM: FOUNDATION AI MODELS FOR ADAPTIVE AND PERSONALIZED ONCOLOGY
The emergence of intelligent cancer learning health systems represents a transformative paradigm in precision oncology, where artificial intelligence (AI), foundation models, multimodal learning, and continuously evolving clinical knowledge converge to create adaptive healthcare ecosystems capable of improving diagnosis, treatment, and survivorship through iterative learning. Conventional oncology frequently relies on fragmented clinical workflows, episodic data collection, and static predictive models that inadequately reflect the dynamic biological evolution of cancer or the continuous accumulation of real-world clinical evidence. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, patient-reported outcomes, biomedical literature, and longitudinal clinical outcomes into unified computational ecosystems. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen learning health systems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite substantial technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent cancer learning health systems, emphasizing foundation AI models as transformative technologies for adaptive and personalized oncology.
Copyright (c) 2024 Dr. Sandeep Menon, Dr. Roshan Thomas (Author) https://creativecommons.org/licenses/by/4.0
DIGITAL ONCOLOGY 5.0: THE CONVERGENCE OF ARTIFICIAL INTELLIGENCE, DIGITAL TWINS, AND PRECISION MEDICINE
Digital Oncology 5.0 represents the next evolutionary stage of cancer medicine, characterized by the convergence of artificial intelligence (AI), digital twins, multimodal foundation models, precision medicine, and intelligent healthcare ecosystems capable of delivering predictive, preventive, personalized, participatory, and continuously adaptive cancer care. Conventional oncology frequently relies on fragmented diagnostic workflows, episodic clinical assessments, and isolated interpretation of imaging, pathology, molecular diagnostics, and clinical information, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen Digital Oncology 5.0 by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite substantial technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, explainability, clinical validation, and equitable implementation. This review provides a comprehensive overview of Digital Oncology 5.0, emphasizing the convergence of artificial intelligence, digital twins, and precision medicine as a transformative paradigm for next-generation personalized cancer care.
Copyright (c) 2024 Dr. Rahul Joseph (Author) https://creativecommons.org/licenses/by/4.0
AI-AUGMENTED PRECISION THERAPEUTICS: FOUNDATION MODELS FOR BIOMARKER DISCOVERY AND PERSONALIZED CANCER TREATMENT
Artificial intelligence (AI) is transforming precision therapeutics by enabling comprehensive integration of molecular, imaging, pathological, and clinical information into intelligent computational ecosystems capable of supporting biomarker discovery and individualized cancer treatment. Conventional precision oncology frequently depends upon fragmented interpretation of genomic alterations, histopathological findings, radiological imaging, and clinical information, limiting comprehensive understanding of tumor biology and therapeutic responsiveness. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen AI-augmented therapeutics by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-augmented precision therapeutics, emphasizing foundation models for biomarker discovery and personalized cancer treatment as a transformative paradigm for next-generation oncology.
Copyright (c) 2024 Dr. Karthika Nair (Author) https://creativecommons.org/licenses/by/4.0
MULTIMODAL LEARNING IN PRECISION ONCOLOGY: BRIDGING IMAGING, MOLECULAR BIOLOGY, AND CLINICAL INTELLIGENCE
Multimodal learning has emerged as one of the most transformative paradigms in precision oncology by enabling artificial intelligence (AI) systems to integrate heterogeneous biomedical data into unified computational representations that support comprehensive cancer diagnosis, prognostic prediction, therapeutic optimization, and personalized clinical decision-making. Conventional oncology frequently relies on isolated interpretation of radiological imaging, digital pathology, molecular diagnostics, and clinical information, limiting the ability to capture the complex biological interactions underlying tumor evolution and therapeutic response. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen multimodal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal learning in precision oncology, emphasizing the integration of imaging, molecular biology, and clinical intelligence as a transformative framework for personalized cancer medicine.
Copyright (c) 2024 Dr. Harish Venkatesh, Dr. Pooja Sinha, Dr. Faizan Ali (Author) https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE–ENABLED COMPUTATIONAL PATHOLOGY: FOUNDATION MODELS FOR NEXT-GENERATION PRECISION CANCER DIAGNOSTICS
Computational pathology has emerged as one of the most transformative applications of artificial intelligence (AI) in modern oncology by enabling automated interpretation of whole-slide images, quantitative characterization of tumor biology, and integration of histopathological information with multimodal biomedical data for precision cancer diagnostics. Conventional pathology relies heavily on expert visual interpretation, which, despite its indispensable role in cancer diagnosis, is susceptible to interobserver variability, increasing workload, and limitations in detecting subtle morphological patterns associated with molecular alterations and clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across digital pathology, radiological imaging, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational pathology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-enabled computational pathology, emphasizing foundation models as transformative technologies for next-generation precision cancer diagnostics.
Copyright (c) 2024 Dr. Abhishek Narayanan (Author) https://creativecommons.org/licenses/by/4.0
INTELLIGENT CANCER DIGITAL TWINS: AI-DRIVEN VIRTUAL PATIENT MODELS FOR PREDICTIVE ONCOLOGY
Intelligent cancer digital twins are emerging as one of the most transformative innovations in precision oncology by enabling continuously evolving virtual representations of individual patients capable of supporting predictive diagnosis, personalized therapeutic planning, adaptive disease monitoring, and evidence-based clinical decision-making. Conventional oncology frequently depends upon fragmented interpretation of radiological imaging, molecular diagnostics, pathological findings, and episodic clinical assessments, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative AI have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into adaptive virtual patient ecosystems. These intelligent digital twins continuously synchronize with evolving patient biology to support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, toxicity prediction, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent cancer digital twins, emphasizing AI-driven virtual patient models as a transformative framework for predictive oncology and personalized cancer medicine.
Copyright (c) 2024 Dr. Kunal Mehta, Dr. Farah Ali (Author) https://creativecommons.org/licenses/by/4.0
SPATIAL OMICS AND FOUNDATION AI MODELS: DECODING TUMOR HETEROGENEITY FOR PRECISION ONCOLOGY
Tumor heterogeneity remains one of the greatest challenges in precision oncology because malignant tissues exhibit extensive spatial, molecular, cellular, and microenvironmental diversity that continuously evolves during disease progression and therapeutic intervention. Recent advances in spatial omics technologies and foundation artificial intelligence (AI) models have created unprecedented opportunities to decode this complexity by integrating high-dimensional molecular, histopathological, imaging, and clinical information into unified computational frameworks. Foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence enable generalized representation learning across spatial transcriptomics, spatial proteomics, spatial metabolomics, digital pathology, radiological imaging, genomics, transcriptomics, epigenomics, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support comprehensive characterization of tumor heterogeneity, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen spatial oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, spatial resolution, interoperability, clinical validation, and equitable implementation. This review provides a comprehensive overview of spatial omics and foundation AI models, emphasizing their role in decoding tumor heterogeneity for precision oncology and personalized cancer medicine.
Copyright (c) 2024 Dr. Pritam Sen (Author) https://creativecommons.org/licenses/by/4.0
CAUSAL ARTIFICIAL INTELLIGENCE IN ONCOLOGY: ENHANCING EXPLAINABILITY AND CLINICAL DECISION-MAKING IN PRECISION MEDICINE
Causal artificial intelligence (AI) is emerging as a transformative paradigm in precision oncology by enabling computational systems to move beyond correlation-based prediction toward causal reasoning that supports transparent, reliable, and clinically actionable decision-making. Conventional machine learning and deep learning models have achieved remarkable success in cancer diagnosis, prognosis, and therapeutic prediction; however, many remain limited by their inability to distinguish causal relationships from statistical associations, thereby reducing interpretability, generalizability, and clinical trust. Recent advances in causal inference, foundation AI models, multimodal transformer architectures, graph neural networks, structural causal models, Bayesian networks, reinforcement learning, and generative artificial intelligence have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into explainable computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, treatment optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support through causal reasoning rather than correlation alone. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen causal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding causal model validation, multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of causal artificial intelligence in oncology, emphasizing its role in enhancing explainability and clinical decision-making for precision medicine.
Copyright (c) 2024 Dr. Raghavendra Joshi (Author) https://creativecommons.org/licenses/by/4.0
FOUNDATION MODELS FOR CANCER INTELLIGENCE: INTEGRATING RADIOLOGY, PATHOLOGY, MULTI-OMICS, AND CLINICAL DECISION SUPPORT
Foundation artificial intelligence (AI) models are transforming modern oncology by enabling comprehensive cancer intelligence through the integration of radiology, pathology, multi-omics, and clinical information into unified computational ecosystems capable of supporting precision diagnosis, prognostic prediction, therapeutic optimization, and personalized clinical decision-making. Conventional artificial intelligence systems have demonstrated considerable success in isolated oncology applications; however, their dependence on task-specific training, fragmented biomedical data, and limited generalizability restrict widespread clinical implementation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, treatment optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen cancer intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation models for cancer intelligence, emphasizing integration of radiology, pathology, multi-omics, and clinical decision support as a transformative framework for personalized cancer medicine.
Copyright (c) 2024 Dr. Shruti Menon, Dr. Naveen Pillai, Dr. KeerthanaIyer (Author) https://creativecommons.org/licenses/by/4.0
AGENTIC ARTIFICIAL INTELLIGENCE IN PRECISION ONCOLOGY: AUTONOMOUS MULTIMODAL SYSTEMS FOR PERSONALIZED CANCER CARE
Agentic artificial intelligence (AI) represents the next frontier in precision oncology by enabling autonomous computational systems capable of perceiving, reasoning, planning, executing, and continuously learning across the entire cancer care continuum. Unlike conventional AI systems that perform isolated predictive tasks, agentic AI integrates foundation models, multimodal learning, digital twins, reinforcement learning, and clinical reasoning into intelligent ecosystems capable of autonomously coordinating diagnostic workflows, therapeutic planning, longitudinal disease monitoring, and personalized clinical decision-making. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and real-world clinical outcomes into unified computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, treatment optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and autonomous planning algorithms further strengthen agentic oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, human oversight, and equitable implementation. This review provides a comprehensive overview of agentic artificial intelligence in precision oncology, emphasizing autonomous multimodal systems for personalized cancer care and clinical translation.
Copyright (c) 2024 Dr. Anirudh Nair (Author) https://creativecommons.org/licenses/by/4.0
AUTONOMOUS PRECISION ONCOLOGY: THE CONVERGENCE OF FOUNDATION AI MODELS, DIGITAL TWINS, AND COMPUTATIONAL CANCER INTELLIGENCE
Autonomous precision oncology represents the next evolutionary stage of computational cancer medicine, where foundation artificial intelligence (AI) models, digital twins, multimodal learning, and computational cancer intelligence converge to create continuously adaptive clinical ecosystems capable of supporting personalized diagnosis, prognostic prediction, therapeutic optimization, and longitudinal disease management. Conventional oncology frequently relies on fragmented diagnostic workflows, episodic clinical assessments, and isolated interpretation of imaging, pathology, molecular diagnostics, and clinical information, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen autonomous oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of autonomous precision oncology, emphasizing the convergence of foundation AI models, digital twins, and computational cancer intelligence as a transformative paradigm for next-generation personalized cancer medicine.
Copyright (c) 2024 Dr. Nirmal Raj (Author) https://creativecommons.org/licenses/by/4.0
TOWARDS GENERALIST ARTIFICIAL INTELLIGENCE IN ONCOLOGY: FOUNDATION MODELS FOR INTEGRATED CANCER DIAGNOSIS AND PERSONALIZED CARE
The rapid evolution of artificial intelligence (AI) has accelerated the transition from task-specific algorithms toward generalist foundation models capable of performing diverse clinical, molecular, and imaging tasks using unified computational intelligence. In oncology, these next-generation systems have the potential to transform cancer diagnosis and personalized care by integrating heterogeneous biomedical information into continuously learning computational ecosystems. Conventional oncology frequently depends on fragmented interpretation of radiological imaging, digital pathology, molecular diagnostics, laboratory investigations, and clinical information, limiting comprehensive understanding of the dynamic biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen generalist oncology intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of generalist artificial intelligence in oncology, emphasizing foundation models for integrated cancer diagnosis and personalized patient care.
Copyright (c) 2024 Dr. Vivek Narayan (Author) https://creativecommons.org/licenses/by/4.0
DIGITAL TWIN–ENABLED PRECISION ONCOLOGY: INTEGRATING LONGITUDINAL PATIENT DATA, MULTI-OMICS, AND ARTIFICIAL INTELLIGENCE
Digital twin technology is rapidly emerging as one of the most transformative innovations in precision oncology by enabling continuously evolving virtual representations of individual cancer patients capable of supporting predictive, personalized, and adaptive clinical decision-making. Traditional oncology frequently depends on fragmented interpretation of radiological imaging, molecular diagnostics, pathological findings, and episodic clinical assessments, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative AI have enabled seamless integration of longitudinal patient data, radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and real-world clinical outcomes into adaptive virtual patient ecosystems. These intelligent digital twins continuously synchronize with evolving patient biology to support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, toxicity prediction, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of digital twin–enabled precision oncology, emphasizing integration of longitudinal patient data, multi-omics, and artificial intelligence as a transformative framework for next-generation personalized cancer medicine.
Copyright (c) 2024 Dr. Zubin Patel (Author) https://creativecommons.org/licenses/by/4.0
NEXT-GENERATION AI IN ONCOLOGY: EXPLAINABLE FOUNDATION MODELS FOR PRECISION DIAGNOSIS, PROGNOSIS, AND TREATMENT OPTIMIZATION
Artificial intelligence (AI) is reshaping modern oncology through the emergence of explainable foundation models capable of integrating heterogeneous biomedical information into intelligent computational ecosystems that support precision diagnosis, prognostic prediction, and individualized therapeutic optimization. Conventional machine learning algorithms have demonstrated remarkable performance in isolated oncology applications; however, their task-specific design, limited interpretability, and dependence on fragmented datasets restrict their widespread clinical implementation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Explainable artificial intelligence (XAI) techniques—including attention visualization, saliency mapping, feature attribution, SHAP (Shapley Additive Explanations), counterfactual reasoning, and uncertainty estimation—provide transparency necessary for clinician confidence, regulatory approval, and trustworthy deployment. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, and agentic AI further strengthen explainable computational oncology through collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of next-generation explainable foundation AI models for precision diagnosis, prognosis, and treatment optimization in modern oncology.
Copyright (c) 2024 Dr. Ravi Chaturvedi (Author) https://creativecommons.org/licenses/by/4.0
PRECISION CANCER INTELLIGENCE: LEVERAGING FOUNDATION MODELS, DIGITAL TWINS, AND MULTIMODAL LEARNING FOR CLINICAL TRANSLATION
Precision cancer intelligence is emerging as a transformative paradigm in modern oncology through the convergence of foundation artificial intelligence (AI) models, digital twins, multimodal learning, and translational clinical intelligence. Conventional oncology frequently relies on fragmented diagnostic modalities and isolated interpretation of molecular, imaging, pathological, and clinical information, thereby limiting comprehensive understanding of the dynamic biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational ecosystems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen precision cancer intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of precision cancer intelligence, emphasizing the convergence of foundation AI, digital twins, and multimodal learning for successful clinical translation and personalized cancer medicine.
Copyright (c) 2024 Dr. MohanaIyer, Dr. Pradeep Joshi, Dr. Aditi Shah (Author) https://creativecommons.org/licenses/by/4.0
AI-POWERED COMPUTATIONAL ONCOLOGY: ADVANCING PERSONALIZED CANCER MEDICINE THROUGH MULTIMODAL DATA FUSION
Computational oncology is undergoing a transformative evolution through the integration of artificial intelligence (AI), multimodal biomedical data fusion, and precision medicine into intelligent computational ecosystems capable of supporting personalized cancer care throughout the disease continuum. Conventional oncology frequently relies on fragmented interpretation of imaging, pathology, molecular diagnostics, and clinical information, thereby limiting comprehensive understanding of the complex biological mechanisms governing tumor evolution and therapeutic response. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-powered computational oncology, emphasizing multimodal data fusion as a transformative framework for advancing personalized cancer medicine.
Copyright (c) 2024 Dr. Arjun Menon, Dr. Roshni Sharma, Mrs. Shilpa Rao, Dr. Danish Khan (Author) https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE–DRIVEN CLINICAL ONCOLOGY INTELLIGENCE: FROM EARLY CANCER DETECTION TO PRECISION THERAPEUTICS
Artificial intelligence (AI) is transforming modern oncology by enabling the development of intelligent clinical ecosystems capable of supporting the entire continuum of cancer care, from early detection and diagnosis to precision therapeutics and long-term survivorship. Conventional oncology frequently relies on fragmented diagnostic modalities, episodic clinical assessments, and static predictive models that inadequately capture the dynamic biological evolution of malignant disease. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen clinical oncology intelligence by enabling collaborative, privacy-preserving, and continuously adaptive biomedical reasoning. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-driven clinical oncology intelligence, emphasizing its role in advancing early cancer detection, precision therapeutics, and personalized cancer medicine.
Copyright (c) 2024 Dr. Charitha Naidu, Dr. Mohit Arora, Mrs. Padmini Shetty (Author) https://creativecommons.org/licenses/by/4.0
MULTIMODAL FOUNDATION MODELS FOR PREDICTIVE ONCOLOGY: UNIFYING RADIOMICS, PATHOMICS, GENOMICS, AND ELECTRONIC HEALTH RECORDS
Predictive oncology is undergoing a paradigm shift through the emergence of multimodal foundation models capable of integrating radiomics, pathomics, genomics, electronic health records (EHRs), and other heterogeneous biomedical data into unified computational frameworks for precision cancer care. Conventional artificial intelligence (AI) models have achieved remarkable success in individual diagnostic or prognostic tasks; however, their dependence on task-specific training and isolated data modalities limits their ability to fully characterize the biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support precision diagnosis, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen predictive oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal foundation models for predictive oncology, emphasizing the integration of radiomics, pathomics, genomics, and electronic health records as a transformative framework for personalized cancer medicine.
Copyright (c) 2024 Dr. Ishaan Kapoor (Author) https://creativecommons.org/licenses/by/4.0
INTELLIGENT DIGITAL TWINS IN ONCOLOGY: REAL-TIME VIRTUAL PATIENT MODELING FOR PERSONALIZED THERAPEUTIC DECISION-MAKING
Digital twin technology is emerging as one of the most transformative innovations in precision oncology by enabling the creation of continuously evolving virtual representations of individual cancer patients capable of supporting real-time therapeutic decision-making. Conventional oncology frequently relies on episodic clinical assessments and fragmented diagnostic information that inadequately capture the dynamic biological evolution of tumors throughout diagnosis, treatment, recurrence, metastasis, and survivorship. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative AI have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into adaptive virtual patient models. These intelligent digital twins continuously synchronize with evolving patient biology, supporting precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, toxicity prediction, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent digital twins in oncology, emphasizing real-time virtual patient modeling for personalized therapeutic decision-making as a transformative paradigm for next-generation precision cancer care.
Copyright (c) 2024 Dr. Farhan Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
FOUNDATION AI MODELS FOR SYSTEMS ONCOLOGY: INTEGRATING MULTI-OMICS, IMAGING, AND CLINICAL INTELLIGENCE FOR PRECISION CANCER CARE
Systems oncology is emerging as a transformative paradigm in precision cancer medicine through the integration of artificial intelligence (AI), multi-omics, medical imaging, digital pathology, and longitudinal clinical intelligence into comprehensive computational ecosystems capable of modeling cancer as a dynamic biological system. Conventional oncology often relies on fragmented analysis of individual diagnostic modalities, limiting the understanding of complex interactions among molecular pathways, tumor microenvironments, host immunity, and clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and real-world clinical evidence. These intelligent systems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen systems oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation AI models for systems oncology, emphasizing the integration of multi-omics, imaging, and clinical intelligence as a transformative framework for precision cancer care.
Copyright (c) 2024 Dr. Harsha Menon, Dr. Bhavana Rao, Dr. Nilesh Patel (Author) https://creativecommons.org/licenses/by/4.0
FUTURE CANCER INTELLIGENCE: CONVERGING FOUNDATION AI, DIGITAL TWINS, MULTIMODAL LEARNING, AND PRECISION ONCOLOGY
Cancer care is entering a new era driven by the convergence of foundation artificial intelligence (AI), digital twins, multimodal learning, and precision oncology into intelligent computational ecosystems capable of continuously modeling disease biology and supporting personalized clinical decision-making. Conventional oncology frequently depends upon fragmented diagnostic workflows, isolated biomarker interpretation, and episodic therapeutic assessment that inadequately reflect the dynamic biological evolution of malignant disease. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and digital twin simulation while continuously learning from evolving biomedical evidence. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance computational oncology by enabling collaborative, privacy-preserving, and adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of future cancer intelligence, emphasizing the convergence of foundation AI, digital twins, multimodal learning, and precision oncology as a transformative paradigm for next-generation personalized cancer care.
Copyright (c) 2024 Dr. Yogesh Rao, Mrs. Pooja Kulshrestha, Dr. Karthik Bhat (Author) https://creativecommons.org/licenses/by/4.0
AUTONOMOUS ONCOLOGY SYSTEMS: FOUNDATION MODELS FOR REAL-TIME CLINICAL INTELLIGENCE AND PERSONALIZED CANCER CARE
Artificial intelligence (AI) is rapidly transforming oncology from a reactive clinical discipline into an autonomous, continuously learning healthcare ecosystem capable of supporting real-time clinical intelligence and personalized cancer care. Conventional clinical decision-making often relies on episodic assessments, fragmented biomedical information, and static predictive models that inadequately reflect the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled the development of autonomous oncology systems capable of integrating radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems continuously learn from evolving biomedical evidence while supporting cancer diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen autonomous oncology by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of autonomous oncology systems, emphasizing foundation AI models for real-time clinical intelligence and personalized cancer care as a transformative paradigm in precision oncology.
Copyright (c) 2024 Dr. Akshay Jain (Author) https://creativecommons.org/licenses/by/4.0
AI-DRIVEN PRECISION CANCER ECOSYSTEMS: FROM LIQUID BIOPSY AND RADIOMICS TO DIGITAL TWINS
Precision oncology is undergoing a profound transformation through the convergence of artificial intelligence (AI), liquid biopsy, radiomics, digital pathology, multi-omics, and digital twin technologies into intelligent computational ecosystems capable of continuously modeling cancer biology across the patient journey. Conventional oncology frequently relies on fragmented diagnostic modalities and episodic clinical assessments that inadequately capture the dynamic evolution of tumors and their interactions with the host environment. Recent advances in foundation AI models, multimodal transformers, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified computational frameworks. These intelligent ecosystems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and digital twin simulation while facilitating evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-driven precision cancer ecosystems, emphasizing the integration of liquid biopsy, radiomics, and digital twins as a transformative paradigm for personalized cancer medicine.
Copyright (c) 2024 Dr. Lakshmi Krishnan (Author) https://creativecommons.org/licenses/by/4.0
INTELLIGENT CANCER FOUNDATION MODELS: INTEGRATING MOLECULAR, IMAGING, AND CLINICAL INTELLIGENCE FOR PRECISION MEDICINE
Artificial intelligence (AI) is transforming precision oncology through the emergence of intelligent foundation models capable of integrating heterogeneous biomedical information into unified computational frameworks supporting personalized cancer care. Conventional machine learning algorithms have demonstrated substantial success in isolated oncology applications; however, their dependence on task-specific training, fragmented analysis of multimodal datasets, and limited generalizability restrict their clinical scalability. Intelligent cancer foundation models overcome these limitations through large-scale self-supervised learning, multimodal transformer architectures, graph neural networks, and generative AI that learn generalized biomedical representations from radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational ecosystems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance foundation model ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational oncology. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent cancer foundation models, emphasizing integration of molecular, imaging, and clinical intelligence as a transformative paradigm for precision medicine.
Copyright (c) 2024 Dr. ChetanMehra (Author) https://creativecommons.org/licenses/by/4.0
THE NEXT GENERATION OF COMPUTATIONAL ONCOLOGY: EXPLAINABLE, MULTIMODAL, AND FEDERATED ARTIFICIAL INTELLIGENCE
Computational oncology is undergoing a profound transformation driven by the convergence of explainable artificial intelligence (XAI), multimodal foundation models, federated learning, and precision medicine. Conventional machine learning approaches have substantially improved cancer diagnosis, prognostic prediction, and therapeutic planning but often remain constrained by limited interpretability, fragmented analysis of heterogeneous biomedical information, and restricted generalizability across healthcare systems. The next generation of computational oncology addresses these limitations through intelligent AI ecosystems capable of integrating radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified patient-specific computational representations. Explainable AI enhances transparency and clinician trust, multimodal learning enables systems-level biological understanding, and federated learning facilitates collaborative model development while preserving patient privacy. Together with graph neural networks, multimodal transformers, self-supervised learning, digital twins, reinforcement learning, multimodal large language models, retrieval-augmented generation, and agentic AI, these technologies establish intelligent computational ecosystems capable of supporting precision diagnosis, biomarker discovery, therapeutic optimization, adaptive disease monitoring, and evidence-based clinical decision support. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of the next generation of computational oncology, emphasizing explainable, multimodal, and federated artificial intelligence as the computational foundation of future precision cancer medicine.
Copyright (c) 2024 Dr. Prakash Nambiar, Dr. Shweta Gupta, Dr. Imran Khan (Author) https://creativecommons.org/licenses/by/4.0
FOUNDATION AI MODELS FOR PRECISION IMMUNOTHERAPY: PREDICTING RESPONSE, RESISTANCE, AND CLINICAL OUTCOMES
Immunotherapy has revolutionized modern oncology by enabling the immune system to recognize and eliminate malignant cells through immune checkpoint inhibitors, adoptive cellular therapies, cancer vaccines, bispecific antibodies, and emerging immunomodulatory strategies. Despite remarkable clinical success, therapeutic responses remain highly heterogeneous because of the extraordinary complexity of tumor–immune interactions, intratumoral heterogeneity, and dynamic evolution of the tumor microenvironment. Foundation artificial intelligence (AI) models have emerged as transformative computational frameworks capable of integrating heterogeneous biomedical information across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, immunomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific representations. These intelligent systems enable comprehensive prediction of immunotherapy response, resistance mechanisms, toxicity, recurrence, and survival while supporting biomarker discovery, digital twin simulation, adaptive treatment planning, and intelligent clinical decision support. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen precision immunotherapy by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation AI models for precision immunotherapy, emphasizing prediction of therapeutic response, resistance, and clinical outcomes as a transformative paradigm in personalized oncology.
Copyright (c) 2024 Dr. Neeraj Khanna, Mrs. Divya Joshi (Author) https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE–ENABLED TUMOR MICROENVIRONMENT MODELING: INTEGRATING SPATIAL BIOLOGY, MULTI-OMICS, AND DIGITAL TWINS
The tumor microenvironment (TME) has emerged as one of the most important determinants of cancer initiation, progression, metastasis, immune evasion, and therapeutic response. Rather than functioning as an isolated population of malignant cells, tumors exist within highly dynamic ecosystems comprising immune cells, stromal fibroblasts, endothelial cells, extracellular matrix components, vascular networks, signaling molecules, metabolites, and microbiota that collectively shape disease evolution. Conventional analytical approaches frequently investigate these biological components independently, limiting comprehensive understanding of tumor ecosystem dynamics. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, spatial computing, and generative AI, have enabled comprehensive computational modeling of the TME through integration of spatial biology, multi-omics, digital pathology, radiological imaging, laboratory biomarkers, liquid biopsy, electronic health records, and longitudinal clinical data. These intelligent systems support biomarker discovery, molecular characterization, prognostic prediction, immunotherapy optimization, adaptive treatment planning, digital twin simulation, and intelligent clinical decision support while advancing systems oncology and precision medicine. Emerging innovations including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational modeling of tumor ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-enabled tumor microenvironment modeling, emphasizing integration of spatial biology, multi-omics, and digital twins as a transformative paradigm for personalized oncology.
Copyright (c) 2024 Dr. Aditya Menon (Author) https://creativecommons.org/licenses/by/4.0
ADAPTIVE FOUNDATION MODELS IN ONCOLOGY: CONTINUOUS LEARNING FOR PERSONALIZED CANCER MANAGEMENT
Artificial intelligence (AI) is transforming oncology from a static decision-support technology into a continuously adaptive computational ecosystem capable of learning from evolving biomedical knowledge and patient-specific clinical trajectories. Conventional machine learning algorithms remain fixed after deployment and often struggle to accommodate the dynamic nature of tumor evolution, therapeutic resistance, changing clinical guidelines, and rapidly expanding biomedical evidence. Adaptive foundation models overcome these limitations through large-scale self-supervised learning, continual learning, multimodal transformer architectures, graph neural networks, reinforcement learning, and generative AI, enabling continuous refinement of predictive performance throughout the cancer care continuum. These intelligent computational systems integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified patient-specific representations. Adaptive foundation models support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, intelligent clinical decision support, and personalized survivorship management while continuously incorporating new biomedical evidence. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen adaptive computational oncology by enabling collaborative, privacy-preserving, and continuously evolving precision medicine. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of adaptive foundation models in oncology, emphasizing continuous learning as a transformative paradigm for personalized cancer management.
Copyright (c) 2024 Dr. Aparna Ghosh (Author) https://creativecommons.org/licenses/by/4.0
CROSS-MODAL ARTIFICIAL INTELLIGENCE FOR PRECISION ONCOLOGY: BRIDGING RADIOLOGY, HISTOPATHOLOGY, GENOMICS, AND CLINICAL RECORDS
Precision oncology has entered an era in which artificial intelligence (AI) is no longer confined to individual diagnostic tasks but instead functions as an integrative computational framework capable of unifying heterogeneous biomedical information across the entire cancer care continuum. Conventional machine learning approaches have achieved remarkable success in isolated domains such as radiology, digital pathology, genomics, or electronic health records; however, their modality-specific design frequently limits comprehensive understanding of tumor biology and clinical decision-making. Recent advances in cross-modal artificial intelligence, including foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have enabled seamless integration of radiological imaging, histopathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, laboratory biomarkers, electronic health records, wearable technologies, and longitudinal clinical outcomes into unified patient-centered computational representations. These intelligent systems facilitate molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and clinical decision support while accelerating precision medicine. Emerging technologies such as multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen cross-modal computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive learning across diverse healthcare environments. Despite extraordinary technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of cross-modal artificial intelligence for precision oncology, emphasizing integration of radiology, histopathology, genomics, and clinical records as the foundation for future intelligent cancer medicine.
Copyright (c) 2024 Dr. Ritesh Kulkarni (Author) https://creativecommons.org/licenses/by/4.0
MULTIMODAL DIGITAL BIOMARKERS IN ONCOLOGY: ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND PERSONALIZED TREATMENT
Digital biomarkers have emerged as one of the most transformative innovations in precision oncology by enabling continuous, objective, and quantitative assessment of cancer biology through integration of multimodal biomedical data. Unlike conventional biomarkers that often rely on isolated molecular or laboratory measurements, multimodal digital biomarkers combine radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, wearable physiological monitoring, laboratory investigations, electronic health records, patient-reported outcomes, and real-world clinical data into comprehensive computational representations of disease. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have fundamentally transformed digital biomarker discovery by enabling generalized biomedical representation learning across heterogeneous data modalities. These intelligent systems facilitate early cancer detection, molecular characterization, prognostic prediction, therapeutic response assessment, immunotherapy optimization, adaptive disease monitoring, digital twin simulation, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen multimodal biomarker ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational oncology. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal digital biomarkers in oncology, emphasizing artificial intelligence as the driving force for early cancer detection and personalized treatment.
Copyright (c) 2024 Dr. Nandita Roy, Dr. Praveen Nair (Author) https://creativecommons.org/licenses/by/4.0