Artificial intelligence and transforming cancer care

Discover Oncology https://doi.org/10.1007/s12672-026-05242-3 Article in Press Artificial intelligence and transforming cancer care Aneesha Mallu Reddy, Gurleen Kaur, Vincent Sean D. Ribaya, Elizabeth Laurize A. Ribaya, Mallu Chenna Reddy, Tariq Shah, Dheeraj Shinde & Gurparsad Singh Suri Received: 25 December 2025 Accepted: 15 May 2026 Cite this article as: Reddy A.M., Kaur G., Ribaya V.S.D. et al. Artificial intelligence and transforming cancer care. Discov Onc (2026). https://doi.org/10.1007/ s12672-026-05242-3 A We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. E R P S S If this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article. I T R E L C IN © The Author(s) 2026. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. ACCEPTED ARTICLEMANUSCRIPT IN PRESS Artificial Intelligence and Transforming Cancer Care Authors Aneesha Mallu Reddy1, Gurleen Kaur2, Vincent Sean D Ribaya1, Elizabeth Laurize A Ribaya1, Mallu Chenna Reddy1, Tariq Shah3-4, Dheeraj Shinde5, Gurparsad Singh Suri1 Affiliations 1 Reddy Care Medical, California, United States 2 California Baptist University, Riverside, United States 3 Western University College of Pharmacy, California, United States 4 Saint John's Physician Partners, California, United States 5 Box Nine Solutions, Satara, India * Corresponding author Gurparsad Singh Suri. E L C I T R A IN S S E R P E.MAIL: Abstract Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized approaches from diagnosis to treatment. This review explores the transformative role of AI across the cancer care continuum, highlighting its contributions, challenges, and future directions. AI has significantly advanced cancer detection and diagnosis by improving the interpretation of medical imaging (CT, MRI, PET scans, digital pathology) and liquid biopsies, allowing for early and accurate identification of tumors and biomarkers. In genomics and molecular profiling, AI facilitates the ACCEPTED ARTICLEMANUSCRIPT IN PRESS analysis of large-scale sequencing data to uncover actionable mutations and support targeted therapy decisions. This review also examines AI-powered prognostic models that integrate clinical, genomic, and electronic health record data to predict outcomes such as survival rates and recurrence risks, allowing for more precise treatment planning. In the therapeutic landscape, AI aids in optimizing radiation dosing, guiding surgical interventions, and predicting individual responses to chemotherapy, immunotherapy, and targeted treatments, thereby reducing uncertainty and improving outcomes. Key limitations, such as data privacy concerns, algorithmic bias, model opacity, and integration hurdles are discussed, along with strategies to S S E R P address them, including explainable AI, standardized validation, and clinician training. Looking ahead, innovations like federated learning, generative AI for drug discovery, and multimodal data integration are poised to enhance precision oncology further. E L C I T R A IN By synthesizing current developments and emerging trends, this review underscores the potential of AI to drive equitable, efficient, and personalized cancer care on a global scale. Keywords: Artificial Intelligence, Cancer Care, Oncology, AI in Healthcare, Machine Learning, Precision Medicine Abbreviations AI: Artificial Intelligence AUC: areas under the curve AUROC: area under the receiver operating characteristic curve CNNs: deep convolutional neural networks csPCa: clinically significant prostate cancer ACCEPTED ARTICLEMANUSCRIPT IN PRESS DL: deep learning EGFR: Epidermal Growth Factor Receptor EHR: electronic health record GDPR: General Data Protection Regulation GMS: genomic mutation signature Grad-CAM: Gradient-weighted Class Activation Mapping HER2: Human Epidermal Growth Factor Receptor 2 HIPAA: Health Insurance Portability and Accountability Act ICG: Indocyanine green ICI: immune checkpoint inhibitor LIME: Local Interpretable Model-agnostic Explanations LLMs: large language models S S E R P LORIS: Logistic Regression-Based Immunotherapy-Response Score LUAD: lung adenocarcinoma LYNA: Lymph Node Assistant E L C I T R A ML: Machine learning IN mRCC: metastatic renal cell carcinoma NHS: National Health Service NIR: near-infrared NKTCL: natural killer/T-cell lymphoma NLST: National Lung Screening Trial NSCLC: non-small cell lung cancer OS: Overall survival PACS: picture archiving and communication systems PFS: Progression-free survival PI-CAI: Prostate Imaging Cancer Artificial Intelligence RCB: residual cancer burden SHAP: SHapley Additive exPlanations ACCEPTED ARTICLEMANUSCRIPT IN PRESS SRH: stimulated Raman histology SRS: stemness-related gene signature TILs: tumor-infiltrating lymphocytes TNBC: triple-negative breast cancer WSIs: whole‐slide images Background Responsible for close to 10 million deaths annually, cancer continues to be a major global health challenge, profoundly affecting healthcare systems, economies, and societies around the world.[1] Despite significant advances in prevention, early detection, and treatment, the inherent complexity and S S intra-tumorEheterogeneity PR IN heterogeneity of cancer continue to confound conventional therapeutic approaches. Tumors vary not only across patients (inter-tumor heterogeneity) but also within individual tumors ( diverse genetic mutations, epigenetic changes, ), driven by microenvironmental influences, and variable metastatic potential.[2], [3] As a result, standard E L C I T R A “one-size-fits-all” therapies such as chemotherapy and radiation often yield variable patient responses, provide limited survi (...truncated)