Jul 2026· International journal of advance research and innovation· 0 citations· 2 references
TL;DR
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.
Abstract
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.
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.
Dr. Harish Venkatesh, Dr. Pooja Sinha, Dr. Faizan Ali· International journal of adv...· 0 citations
This review provides a comprehensive overview of AI-enabled computational pathology, emphasizing foundation models as transformative technologies for next-generation precision cancer diagnostics.
Dr. Abhishek Narayanan· International journal of adv...· 0 citations
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.
Kunal Mehta, Farah Ali· International journal of adv...· 0 citations
Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.
Precision phenomics is introduced as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management and has the potential to transform lung cancer research and improve patient outcomes.
Precision oncology has emerged as a transformative paradigm in cancer diagnosis and therapeutic decision-making through the integration of molecular diagnostics, genomic profiling, artificial intelligence (AI), and translational medicine. Conventional oncology approaches based primarily on histopathological classification frequently fail to address the extensive intertumoral and intratumoral heterogeneity that contributes to therapeutic resistance, metastatic progression, and variable clinical outcomes. Recent advances in next-generation sequencing (NGS), circulating tumor DNA (ctDNA)-based liquid biopsy technologies, transcriptomics, multi-omics integration, and AI-assisted computational oncology have significantly improved biomarker discovery, molecular surveillance, therapeutic response prediction, and personalized treatment strategies. Machine learning and deep learning algorithms are increasingly utilized for tumor classification, digital pathology, radiomics, survival prediction, biomarker-guided therapeutic optimization, and recurrence monitoring. ctDNA-based liquid biopsy platforms further provide minimally invasive approaches for longitudinal tumor profiling, minimal residual disease (MRD) detection, clonal evolution analysis, and real-time therapeutic response monitoring. Molecular biomarkers including EGFR mutations, HER2 amplification, KRAS mutations, microsatellite instability (MSI), tumor mutational burden (TMB), and angiogenic signaling markers are increasingly incorporated into precision therapeutic frameworks. Additionally, AI-assisted integration of genomics, transcriptomics, proteomics, epigenomics, and metabolomics has accelerated translational precision medicine. Despite substantial advances, major challenges remain, including biomarker standardization, AI interpretability limitations, sequencing costs, data heterogeneity, and clinical implementation barriers. This review discusses recent advances in AI-assisted precision oncology, molecular biomarker systems, ctDNA technologies, therapeutic resistance mechanisms, translational oncology, and emerging multi-omics frameworks. The review also highlights future directions involving AI-assisted molecular surveillance, computational oncology platforms, and personalized cancer therapeutics.
M. Mahmudul, Hasan Bhuyain, Fariya Chowdhury· Journal of Medical and Healt...· 0 citations
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