International Journal of Education & Applied Sciences Research
Print ISSN : 2349 –4808
Online ISSN : 2349 –2899
Frequency : Continuous
Current Issue : Volume 11 , Issue 2
2024
Dr.Nivedita Bose
Dr.Nivedita Bose, Professor,Department of Oncology, Sree Balaji Medical College, Chennai, India.
Published Online : 2024-12-30
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ABSTRACT
Artificial intelligence (AI) has become a central pillar of precision oncology by enabling computational interpretation of increasingly complex biomedical data generated throughout the cancer care continuum. Conventional machine learning algorithms have demonstrated remarkable success in individual oncology applications but remain constrained by task-specific training, fragmented analysis of heterogeneous datasets, and limited generalizability across diverse clinical environments. Clinical foundation models represent the next generation of AI, leveraging large-scale self-supervised learning to acquire generalized biomedical representations that can be adapted across numerous oncology tasks. These models integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, electronic health records, wearable technologies, and longitudinal clinical outcomes into unified patient-centered computational frameworks. Foundation models are transforming cancer diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Recent advances in multimodal transformers, graph neural networks, multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI have further strengthened these computational ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive precision oncology. 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 clinical foundation models in oncology, emphasizing their transformative role in cancer diagnosis, prognostic prediction, therapeutic decision-making, and the future of personalized cancer medicine.[1]
Keywords:Foundation models, Clinical artificial intelligence, Precision oncology, Cancer diagnosis, Therapeutic decision-making, Digital pathology, Radiomics, Multi-omics, Clinical decision support, Personalized medicine.
