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Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges.

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Title: Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges.
Authors: Kulkarni Kale U; Citadel Precision Medicine India (CPMI), Hyderabad, Telangana, India.; Citadel Precision Medicine LLP (CPML), Iselin, NJ., Vemulapalli G; City of Hope, Duarte, CA.
Source: Cancer journal (Sudbury, Mass.) [Cancer J] 2025 Nov-Dec 01; Vol. 31 (6). Date of Electronic Publication: 2025 Nov 18.
Publication Type: Journal Article; Review
Language: English
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 100931981 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1540-336X (Electronic) Linking ISSN: 15289117 NLM ISO Abbreviation: Cancer J Subsets: MEDLINE
Imprint Name(s): Publication: 2007- : Sudbury, MA : Lippincott Williams & Wilkins
Original Publication: Sudbury, MA : Jones and Bartlett Publishers, c2000-
MeSH Terms: Neoplasms*/therapy , Neoplasms*/diagnosis , Artificial Intelligence* , Workflow* , Continuity of Patient Care*, Humans ; Precision Medicine/methods
Abstract: Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.
Cancer cases are projected to hit 35 million worldwide by 2050, posing a significant burden on health care systems. The cancer care continuum has evolved to precision medicine practices, provisioning personalized treatments based on multimodal and multiomics data. Contextual analysis of such diverse, voluminous, spatiotemporal patient data is beyond human cognitive capacity. Artificial Intelligence (AI) technologies are reshaping the data mining paradigm in healthcare by delivering novel data-led insights in real time. AI-based methods for cancer risk predictions, diagnosis, prognosis, and therapeutics are developed, validated, and approved, indicating readiness for integration in clinical workflows. Additional validation of AI models using real-world data representing diverse populations is recommended to address clinical, technical, regulatory, ethical, and legal challenges, along with trust issues. Integrating AI tools into cancer care workflows to augment clinical decision-making, without compromising clinical autonomy and patient safety, is essential to address the increasing demand for cancer care by 2050.
(Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)
References: Ferlay J, Ervik M, Lam F, et al. eds, Global Cancer Observatory: Cancer Today (Version 10). International Agency for Research on Cancer; 2024. Accessed February 1, 2024. https://gco.iarc.who.int/today.
Chen S, Cao Z, Prettner K, et al. Estimates and projections of the global economic cost of 29 cancers in 204 countries and territories from 2020 to 2050. JAMA Oncol. 2023;9:465–472.
Bray F, Laversanne M, Weiderpass E, et al. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer. 2021;127:3029–3030.
Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York, NY: Basic Books; 2019.
Malik A, Patel P, Ehsan L, et al. Ten simple rules for engaging with artificial intelligence in biomedicine. PLoS Comput Biol. 2021;17:e1008531.
Shulman LN, Hricak H, Eckhardt SG. Challenges and opportunities for the US Oncology Workforce. JAMA Oncol. 2025;11:961–962.
Sinsky CA, Privitera MR. Creating a “Manageable Cockpit” for clinicians: a shared responsibility. JAMA Intern Med. 2018;178:741–742.
Meskó B. The real era of the art of medicine begins with artificial intelligence. J Med Internet Res. 2019;21:e16295.
Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12:573–576.
Lin SY, Mahoney MR, Sinsky CA. Ten ways artificial intelligence will transform primary care. J Gen Intern Med. 2019;34:1626–1630.
Rashidi HH, Hu B, Pantanowitz J, et al. Statistics of generative artificial intelligence and nongenerative predictive analytics machine learning in medicine. Mod Pathol. 2025;38:100663.
Bhinder B, Gilvary C, Madhukar NS, et al. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021;11:900–915.
Rashidi HH, Pantanowitz J, Hanna MG, et al. Introduction to artificial intelligence and machine learning in pathology and medicine: generative and nongenerative artificial intelligence basics. Mod Pathol. 2025;38:100688.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444.
Fountzilas E, Pearce T, Baysal MA, et al. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. NPJ Digit Med. 2025;8:75.
Scott IA, Zuccon G. The new paradigm in machine learning—foundation models, large language models and beyond: a primer for physicians. Intern Med J. 2024;54:705–715.
Guo F, Guan R, Li Y, et al. Foundation models in bioinformatics. Natl Sci Rev. 2025;12:nwaf028.
Klauschen F, Dippel J, Keyl P, et al. Toward explainable artificial intelligence for precision pathology. Annu Rev Pathol. 2024;19:541–570.
Pantanowitz L, Pearce T, Abukhiran I, et al. Nongenerative artificial intelligence in medicine: advancements and applications in supervised and unsupervised machine learning. Mod Pathol. 2025;38:100680.
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3:118.
Lee B, Patel S, Favorito C, et al. Development and commercialization pathways of AI medical devices in the United States: implications for safety and regulatory oversight. NEJM AI. 2025;2. doi: 10.1056/AIra2500061.
Kim J, Harper A, McCormack V, et al. Global patterns and trends in breast cancer incidence and mortality across 185 countries. Nat Med. 2025;31:1154–1162.
Goddard KAB, Feuer EJ, Mandelblatt JS, et al. Estimation of cancer deaths averted from prevention, screening, and treatment efforts, 1975-2020. JAMA Oncol. 2025;11:162–167.
Coles CE, Earl H, Anderson BO, et al; Lancet Breast Cancer Commission. The Lancet Breast Cancer Commission. Lancet. 2024;403:1895–1950.
Eisenstein M. How AI is helping to boost cancer screening. Nature. 2025;640:S62–S64.
Gentile F, Malara N. Artificial intelligence for cancer screening and surveillance. ESMO Real World Data Digital Oncol. 2024;5:100046.
Baughan N, Douglas L, Giger ML. Past, present, and future of machine learning and artificial intelligence for breast cancer screening. J Breast Imaging. 2022;4:451–459.
Achour N, Zapata T, Saleh Y, et al. The role of AI in mitigating the impact of radiologist shortages: a systematised review. Health Technol (Berl). 2025;15:489–501.
Uwimana A, Gnecco G, Riccaboni M. Artificial intelligence for breast cancer detection and its health technology assessment: a scoping review. Comput Biol Med. 2025;184:109391.
Altobelli E, Angeletti PM, Ciancaglini M, et al. The future of breast cancer organized screening program through artificial intelligence: a scoping review. Healthcare (Basel). 2025;13:378.
Hernström V, Josefsson V, Sartor H, et al. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. Lancet Digit Health. 2025;7:e175–e183.
Marinovich ML, Wylie E, Lotter W, et al. Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection. EBioMedicine. 2023;90:104498.
Houssami N, Marinovich ML. AI for mammography: making double screen-reading history. Lancet Digit Health. 2025;7:e168–e169.
Poon EG, Lemak CH, Rojas JC, et al. Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges. J Am Med Inform Assoc. 2025;32:1093–1100.
Huang Y, Huang S, Liu Z. Multi-task learning-based feature selection and classification models for glioblastoma and solitary brain metastases. Front Oncol. 2022;12:1000471.
Wang H, Sarrami A, Wu J, et al. Multimodal pediatric lymphoma detection using PET and MRI. AMIA Annu Symp Proc. 2024;2023:736–743.
Riaz IB, Ashraf N, Harris GJ, et al. Deep learning to estimate RECIST in patients with cancer treated in real-world settings. J Clin Oncol. 2023;41:1564.
Kraus KM, Oreshko M, Schnabel JA, et al. Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: the relevance of fractionation. Lung Cancer. 2024;189:107507.
Hu S, Li Y, Fan X. Predictive value of simulated CT radiomics combined with ipsilateral lung dosimetry parameters for radiation pneumonitis in patients with esophageal cancer: a machine learning-based retrospective study. Int J Gen Med. 2024;17:4127–4140.
Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med. 2020;3:126.
Abels E, Pantanowitz L, Aeffner F, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol. 2019;249:286–294.
Berbís MA, McClintock DS, Bychkov A, et al. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. EBioMedicine. 2023;88:104427.
McCaffrey C, Jahangir C, Murphy C, et al. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn. 2024;24:363–377.
Haghighat M, Browning L, Sirinukunwattana K, et al. Automated quality assessment of large digitised histology cohorts by artificial intelligence. Sci Rep. 2022;12:5002.
Hida AI, Omanovic D, Pedersen L, et al. Automated assessment of Ki-67 in breast cancer: the utility of digital image analysis using virtual triple staining and whole slide imaging. Histopathology. 2020;77:471–480.
Yousif M, van Diest PJ, Laurinavicius A, et al. Artificial intelligence applied to breast pathology. Virchows Arch. 2022;480:191–209.
McGenity C, Clarke EL, Jennings C, et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med. 2024;7:114.
Gaffney H, Mirza KM. Pathology in the artificial intelligence era: guiding innovation and implementation to preserve human insight. Acad Pathol. 2025;12:100166.
Kapil A, Spitzmüller A, Brieu N, et al. HER2 quantitative continuous scoring for accurate patient selection in HER2 negative trastuzumab deruxtecan treated breast cancer. Sci Rep. 2024;14:12129.
Garassino MC, Sands J, Paz-Ares L, et al. Normalized membrane ratio of TROP2 by quantitative continuous scoring is predictive of clinical outcomes in TROPION-Lung01. J Thoraic Oncology. 2024;19:S2–S3.
Lee RY, Ng CW, Rajapakse MP, et al. The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI. Front Oncol. 2023;13:1172314.
Harkos C, Hadjigeorgiou AG, Voutouri C, et al. Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer. Nat Rev Cancer. 2025;25:324–340.
Carvalho R, Zander T, Barroso VM, et al. AI-based tumor-stroma ratio quantification algorithm: comprehensive evaluation of prognostic role in primary colorectal cancer. Virchows Arch. 2025. doi: 10.1007/s00428-025-04048-y.
Xiang J, Wang X, Zhang X, et al. A vision–language foundation model for precision oncology. Nature. 2025;638:769–778.
Yala A, Mikhael PG, Strand F, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13:eaba4373.
Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550–1558.
Martí-Bonmatí L, Alberich-Bayarri Á, Ladenstein R, et al. PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp. 2020;4:22.
Samala RK, Drukker K, Shukla-Dave A, et al. AI and machine learning in medical imaging: key points from development to translation. BJR Artif Intell. 2024;1:ubae006.
Kneepkens E, Bakx N, van der Sangen M, et al. Clinical evaluation of two AI models for automated breast cancer plan generation. Radiat Oncol. 2022;17:25.
Chong PL, Vaigeshwari V, Mohammed Reyasudin BK, et al. Integrating artificial intelligence in healthcare: applications, challenges, and future directions. Future Sci OA. 2025;11:2527505.
Guedes J, Szadai L, Woldmar N, et al. The melanoma MEGA-study: integrating proteogenomics, digital pathology, and AI-analytics for precision oncology. J Proteomics. 2025;319:105482.
You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther. 2022;7:156.
Wieder R, Adam N. Drug repositioning for cancer in the era of AI, big omics, and real-world data. Crit Rev Oncol Hematol. 2022;175:103730.
Le MHN, Nguyen PK, Nguyen TPT, et al. An in-depth review of AI-powered advancements in cancer drug discovery. Biochim Biophys Acta Mol Basis Dis. 2025;1871:167680.
Pourmousa M, Jain S, Barnaeva E, et al. AI-driven discovery of synergistic drug combinations against pancreatic cancer. Nat Commun. 2025;16:4020.
Mao Y, Shangguan D, Huang Q, et al. Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges. Mol Cancer. 2025;24:123.
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2025;596:583–589.
Yang W, Hicks DR, Ghosh A, et al. Design of high-affinity binders to immune modulating receptors for cancer immunotherapy. Nat Commun. 2025;16:2001.
Jin Q, Wang Z, Floudas CS, et al. Matching patients to clinical trials with large language models. Nat Commun. 2024;15:9074.
Gupta S, Basu A, Nievas M, et al. PRISM: Patient Records Interpretation for Semantic clinical trial Matching system using large language models. NPJ Digit Med. 2024;7:305.
Ma S, Wang Y, Wagner J, et al. Predicting accrual success for better clinical trial resource allocation. Sci Rep. 2025;15:3879.
Azenkot T, Rivera DR, Stewart MD, et al. Artificial intelligence and machine learning innovations to improve design and representativeness in oncology clinical trials. Am Soc Clin Oncol Educ Book. 2025;45:e473590.
Everett SS, Bunning BJ, Jain P, et al. From tool to teammate: a randomized controlled trial of clinician-AI collaborative workflows for diagnosis. medRxiv [Preprint]. 2025. doi: 10.1101/2025.06.07.25329176.
McDuff D, Schaekermann M, Tu T, et al. Towards accurate differential diagnosis with large language models. Nature. 2025;642:451–457.
Hager P, Jungmann F, Holland R, et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat Med. 2024;30:2613–2622.
Liu X, Liu H, Yang G, et al. A generalist medical language model for disease diagnosis assistance. Nat Med. 2025;31:932–942.
Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med. 2024;30:1134–1142.
Tang L, Sun Z, Idnay B, et al. Evaluating large language models on medical evidence summarization. NPJ Digit Med. 2023;6:158.
Ji Y, Ma W, Sivarajkumar S, et al. Mitigating the risk of health inequity exacerbated by large language models. NPJ Digit Med. 2025;8:246.
Kotter E, Ranschaert E. Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow. Eur Radiol. 2021;31:5–7.
Chang JS, Kim H, Baek ES, et al. Continuous multimodal data supply chain and expandable clinical decision support for oncology. NPJ Digit Med. 2025;8:128.
Ueda D, Kakinuma T, Fujita S, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024;42:3–15.
Yoshiura T, Kiryu S. FAIR: a recipe for ensuring fairness in healthcare artificial intelligence. Jpn J Radiol. 2024;42:1–2.
Goktas P, Grzybowski A. Shaping the future of healthcare: ethical clinical challenges and pathways to trustworthy AI. J Clin Med. 2025;14:1605.
Olaye IM, Seixas AA. The gap between AI and bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice. J Med Internet Res. 2023;25:e32962.
Mennella C, Maniscalco U, De Pietro G, et al. Ethical and regulatory challenges of AI technologies in healthcare: a narrative review. Heliyon. 2024;10:e26297.
Jackson BR, Sendak MP, Solomonides A, et al. Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model. J Am Med Inform Assoc. 2025;32:404–407.
Kurnat-Thoma EL. Patient safety and healthcare quality of U.S. laboratory developed tests (LDTs) in the AI/ML era of precision medicine. Front Mol Biosci. 2024;11:1407513.
Stetson PD, Choy J, Summerville N, et al. Responsible Artificial Intelligence governance in oncology. NPJ Digit Med. 2025;8:407.
Shah NH, Halamka JD, Saria S, et al. A nationwide network of health AI assurance laboratories. JAMA. 2024;331:245–249.
Contributed Indexing: Keywords: Artificial intelligence; clinical data mining; clinical decision support; clinical workflow integration; deep learning; ethics; machine learning; personalized medicine; precision oncology
Entry Date(s): Date Created: 20251118 Date Completed: 20251118 Latest Revision: 20251118
Update Code: 20251119
DOI: 10.1097/PPO.0000000000000799
PMID: 41252130
Database: MEDLINE
Description
Abstract:Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.<br />Cancer cases are projected to hit 35 million worldwide by 2050, posing a significant burden on health care systems. The cancer care continuum has evolved to precision medicine practices, provisioning personalized treatments based on multimodal and multiomics data. Contextual analysis of such diverse, voluminous, spatiotemporal patient data is beyond human cognitive capacity. Artificial Intelligence (AI) technologies are reshaping the data mining paradigm in healthcare by delivering novel data-led insights in real time. AI-based methods for cancer risk predictions, diagnosis, prognosis, and therapeutics are developed, validated, and approved, indicating readiness for integration in clinical workflows. Additional validation of AI models using real-world data representing diverse populations is recommended to address clinical, technical, regulatory, ethical, and legal challenges, along with trust issues. Integrating AI tools into cancer care workflows to augment clinical decision-making, without compromising clinical autonomy and patient safety, is essential to address the increasing demand for cancer care by 2050.<br /> (Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)
ISSN:1540-336X
DOI:10.1097/PPO.0000000000000799