ESMO guidance on the use of Large Language Models in Clinical Practice (ELCAP)
Open accessAbstract
BACKGROUND: Large language models (LLMs) are rapidly being integrated into health care, with substantial implications for oncology practice. The European Society for Medical Oncology (ESMO) developed the ESMO guidance on the use of Large Language Models in Clinical Practice (ELCAP) to provide a structured framework and basic guidance for their safe and effective application in oncology. PATIENTS AND METHODS: Between November 2024 and February 2025, a multidisciplinary group of 20 experts convened under the ESMO Real World Data and Digital Health Task Force. Using literature review and a Delphi consensus process, the panel defined three categories of LLM use in oncology: type 1 (patient-facing applications), type 2 [health care professional (HCP)-facing applications], and type 3 (background institutional systems). Consensus statements were developed for each type to provide basic practical guidance. RESULTS: ELCAP highlights opportunities such as improved patient education and symptom management, streamlined clinical workflows, and enhanced data processing. At the same time, it addresses challenges including data privacy, algorithmic bias, regulatory compliance, and the risk of unsupervised use. The framework emphasises human oversight, protection of patient privacy, and alignment with clinical and ethical standards. Patient-facing tools should complement, not replace, professional advice and should be embedded in supervised care pathways. HCP-facing and background systems may improve efficiency and decision support but require systematic validation, transparency, and continuous monitoring. CONCLUSIONS: ELCAP provides a three-tier framework and basic practical guidance for LLM use in oncology. ESMO supports efforts to use this framework to improve patient care, but warns against unsupervised or unvalidated use.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI tools may assist with cross-referencing increasingly complex oncology guidelines, particularly when multiple variables must be considered simultaneously.
“AI tools may assist with cross-referencing increasingly complex oncology guidelines, particularly when multiple variables must be considered simultaneously.11”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [11]
In “Examples of ELCAP type 2 AI systems” (page not available), the paper says LLMs have shown promise in pointing clinicians to the correct guidelines; “Drafting documents” also describes tools that help identify relevant guidelines. It does not specifically say they cross-reference guidelines when multiple variables must be considered simultaneously, so that qualification is not established by our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe swift integration of LLMs into clinical workflows has led to the release of first official guidelines for their use in practice.
“The swift integration of LLMs into clinical workflows has also led to the release of first official guidelines for their use in practice [11].”
From Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation (2026) · cites this paper as [11]
The Introduction (page unavailable) describes rapid LLM uptake and says ESMO developed guidance for their use in oncology; the practical-guidance section provides recommendations for clinicians. The paper does not establish that these were the first official guidelines, and its guidance is specific to oncology rather than clinical practice generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLLMs may facilitate equitable access to information across linguistically diverse populations.
“They may also facilitate equitable access to information across linguistically diverse populations and support patient-facing education frameworks such as European Society for Medical Oncology Guidance on the Use of Large Language Models in Clinical Practice guidance.11”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Diamond, I. R., Grant, R. C., Feldman, B. M., Pencharz, P. B., Ling, S. C., Moore, A. M., & Wales, P. W. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.12.002 · Cited by 3,740 · see citing works
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · see citing works
Anxiety and depression after cancer diagnosis: Prevalence rates by cancer type, gender, and age
Linden, W., Vodermaier, A., MacKenzie, R., & Greig, D. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.03.025 · Cited by 1,245 · see citing works
The future landscape of large language models in medicine
Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J.-N., Laleh, N. G., Löffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00370-1 · Cited by 1,045 · see citing works
Self-citationAdapted large language models can outperform medical experts in clinical text summarization
Van Veen, D., Van Uden, C., Blankemeier, L., Delbrouck, J.-B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerová, A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02855-5 · Cited by 891 · see citing works
Structured information extraction from scientific text with large language models
Dagdelen, J., Dunn, A., Lee, S., Walker, N., Rosen, A. S., Ceder, G., Persson, K. A., & Jain, A. (2024) · Nature Communications · doi:10.1038/s41467-024-45563-x · Cited by 771 · see citing works
The TRIPOD-LLM reporting guideline for studies using large language models
Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G., Demner-Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03425-5 · Cited by 615 · see citing works
Self-citationZack, T., Lehman, E., Suzgun, M., Rodriguez, J. A., Celi, L. A., Gichoya, J., Jurafsky, D., Szolovits, P., Bates, D. W., Abdulnour, R.-E. E., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00225-x · Cited by 510 · see citing works
Ambient artificial intelligence scribes to alleviate the burden of clinical documentation.
Tierney, A. A., Gayre, G., Hoberman, B., Mattern, B., Ballesca, M., Kipnis, P., Liu, V., & Lee, K. (2024) · NEJM Catalyst · doi:10.1056/cat.23.0404 · Cited by 400 · see citing works
Towards conversational diagnostic artificial intelligence
Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08866-7 · Cited by 366 · see citing works
Current applications and challenges in large language models for patient care: a systematic review
Busch, F., Hoffmann, L., Rueger, C., van Dijk, E. H., Kader, R., Ortiz-Prado, E., Makowski, M. R., Saba, L., Hadamitzky, M., Kather, J. N., et al. (2025) · Communications Medicine · doi:10.1038/s43856-024-00717-2 · Cited by 358 · see citing works
Self-citationAdams, L. C., Truhn, D., Busch, F., Kader, A., Niehues, S. M., Makowski, M. R., & Bressem, K. K. (2023) · Radiology · doi:10.1148/radiol.230725 · Cited by 339 · see citing works
Large language models in patient education: a scoping review of applications in medicine
Aydin, S., Karabacak, M., Vlachos, V., & Margetis, K. D. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1477898 · Cited by 333 · see citing works
Leveraging large language models for decision support in personalized oncology
Benary, M., Wang, X. D., Schmidt, M., Soll, D., Hilfenhaus, G., Nassir, M., Sigler, C., Knödler, M., Keller, U., Beule, D., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.43689 · Cited by 306 · see citing works
Matching patients to clinical trials with large language models
Jin, Q., Wang, Z., Floudas, C. S., Chen, F., Gong, C., Bracken-Clarke, D., Xue, E., Yang, Y., Sun, J., & Lu, Z. (2024) · Nature Communications · doi:10.1038/s41467-024-53081-z · Cited by 287 · see citing works
Self-citationRandomized Trial of a Generative AI Chatbot for Mental Health Treatment
Heinz, M. V., Mackin, D. M., Trudeau, B. M., Bhattacharya, S., Wang, Y., Banta, H. A., Jewett, A. D., Salzhauer, A. J., Griffin, T. Z., & Jacobson, N. C. (2025) · NEJM AI · doi:10.1056/aioa2400802 · Cited by 287 · see citing works
Large Language Models for Chatbot Health Advice Studies: A Systematic Review
Huo, B., Boyle, A., Marfo, N., Tangamornsuksan, W., Steen, J. P., McKechnie, T., Lee, Y., Mayol, J., Antoniou, S. A., Thirunavukarasu, A. J., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.57879 · Cited by 283 · see citing works
Burnout Related to Electronic Health Record Use in Primary Care
Budd, J. (2023) · Journal of Primary Care & Community Health · doi:10.1177/21501319231166921 · Cited by 223 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Automated Real-World Data Integration Improves Cancer Outcome Prediction
Jee, J., Fong, C., Pichotta, K., Tran, T. N., Luthra, A., Waters, M., Fu, C., Altoe, M., Liu, S.-Y., Maron, S. B., et al. (2024) · Nature · doi:10.1038/s41586-024-08167-5 · Cited by 188 · see citing works
Ferber, D., El Nahhas, O. S. M., Wölflein, G., Wiest, I. C., Clusmann, J., Leßmann, M.-E., Foersch, S., Lammert, J., Tschochohei, M., Jäger, D., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-00991-6 · Cited by 175 · see citing works
Self-citationPrivacy-preserving large language models for structured medical information retrieval
Wiest, I. C., Ferber, D., Zhu, J., van Treeck, M., Meyer, S. K., Juglan, R., Carrero, Z. I., Paech, D., Kleesiek, J., Ebert, M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01233-2 · Cited by 170 · see citing works
Self-citationGenerative artificial intelligence in primary care: an online survey of UK general practitioners
Blease, C., Locher, C., Gaab, J., Hägglund, M., & Mandl, K. D. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101102 · Cited by 169 · see citing works
Freyer, O., Wiest, I. C., Kather, J. N., & Gilbert, S. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00124-9 · Cited by 162 · see citing works
Self-citationThe rise of agentic AI teammates in medicine
Zou, J., & Topol, E. J. (2025) · The Lancet · doi:10.1016/s0140-6736(25)00202-8 · Cited by 138 · see citing works
Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying
Soroush, A., Glicksberg, B. S., Zimlichman, E., Barash, Y., Freeman, R., Charney, A. W., Nadkarni, G. N., & Klang, E. (2024) · NEJM AI · doi:10.1056/aidbp2300040 · Cited by 137 · see citing works
Large language models should be used as scientific reasoning engines, not knowledge databases
Truhn, D., Reis-Filho, J. S., & Kather, J. N. (2023) · Nature Medicine · doi:10.1038/s41591-023-02594-z · Cited by 102 · see citing works
Self-citationGPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines
Ferber, D., Wiest, I. C., Wölflein, G., Ebert, M. P., Beutel, G., Eckardt, J.-N., Truhn, D., Springfeld, C., Jäger, D., & Kather, J. N. (2024) · NEJM AI · doi:10.1056/aics2300235 · Cited by 92 · see citing works
Self-citationUsing ChatGPT to Facilitate Truly Informed Medical Consent
Mirza, F. N., Tang, O. Y., Connolly, I. D., Abdulrazeq, H. A., Lim, R. K., Roye, G. D., Priebe, C. J., Chandler, C., Libby, T. J., Groff, M. W., et al. (2024) · NEJM AI · doi:10.1056/aics2300145 · Cited by 90 · see citing works
Large language model use in clinical oncology
Carl, N., Schramm, F., Haggenmüller, S., Kather, J. N., Hetz, M. J., Wies, C., Michel, M. S., Wessels, F., & Brinker, T. J. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00733-4 · Cited by 85 · see citing works
Self-citationCarrera, P., Curigliano, G., Santini, D., Sharp, L., Chan, R. J., Pisu, M., Perrone, F., Karjalainen, S., Numico, G., Cherny, N., et al. (2024) · ESMO Open · doi:10.1016/j.esmoop.2024.102992 · Cited by 70 · see citing works
Self-citationExpert-Guided Large Language Models for Clinical Decision Support in Precision Oncology
Lammert, J., Dreyer, T. F., Mathes, S., Kuligin, L., Borm, K. J., Schatz, U. A., Kiechle, M. B., Lörsch, A. M., Jung, J., Lange, S., et al. (2024) · JCO Precision Oncology · doi:10.1200/po-24-00478 · Cited by 70 · see citing works
Self-citationDaly, B., Nicholas, K. J., Flynn, J. R., Silva, N., Panageas, K. S., Mao, J. J. J., Gazit, L., Gorenshteyn, D., Sokolowski, S. S., Newman, T., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.1078 · Cited by 69 · see citing works
Zero-Shot Clinical Trial Patient Matching with LLMs
Wornow, M., Lozano, A., Dash, D., Jindal, J., Mahaffey, K. W., & Shah, N. H. (2025) · NEJM AI · doi:10.1056/aics2400360 · Cited by 59 · see citing works
Large language models for oncological applications
Sorin, V., Barash, Y., Konen, E., & Klang, E. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04824-w · Cited by 58 · see citing works
Gupta, S., Basu, A., Nievas, M., Thomas, J., Wolfrath, N., Ramamurthi, A., Taylor, B., Kothari, A. N., Schwind, R., Miller, T. M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01274-7 · Cited by 52 · see citing works
Verlingue, L., Boyer, C., Olgiati, L., Brutti-Mairesse, C., Morel, D., & Blay, J. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.101064 · Cited by 50 · see citing works
Self-citationExamining human-AI interaction in real-world healthcare beyond the laboratory
Wekenborg, M., Gilbert, S., & Kather, J. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01559-5 · Cited by 49 · see citing works
Self-citationRadioRAG: Online Retrieval–Augmented Generation for Radiology Question Answering
Arasteh, S. T., Lotfinia, M., Bressem, K. K., Siepmann, R. M., Adams, L. C., Ferber, D., Kuhl, C. K., Kather, J. N., Nebelung, S., & Truhn, D. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.240476 · Cited by 46 · see citing works
Self-citationWen, B., Norel, R., Liu, J. C., Stappenbeck, T. S., Zulkernine, F. H., & Chen, H. (2024) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh62654.2024.00027 · Cited by 41 · see citing works
GPT-4 in a Cancer Center — Institute-Wide Deployment Challenges and Lessons Learned
Umeton, R., Kwok, A., Maurya, R., Leco, D., Lenane, N., Willcox, J., Abel, G. A., Tolikas, M., & Johnson, J. M. (2024) · NEJM AI · doi:10.1056/aics2300191 · Cited by 37 · see citing works
Self-citationRecommendations to Ensure Safety of AI in Real-World Clinical Care
Sittig, D. F., & Singh, H. (2024) · JAMA · doi:10.1001/jama.2024.24598 · Cited by 35 · see citing works
Assessment of Large Language Models (LLMs) in Decision-Making Support for Gynecologic Oncology
Gumilar, K. E., Indraprasta, B. R., Faridzi, A. S., Wibowo, B. M., Herlambang, A., Rahestyningtyas, E., Irawan, B., Tambunan, Z., Bustomi, A. F., Brahmantara, B. N., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.10.050 · Cited by 31 · see citing works
Large language models could make natural language again the universal interface of healthcare
Kather, J. N., Ferber, D., Wiest, I. C., Gilbert, S., & Truhn, D. (2024) · Nature Medicine · doi:10.1038/s41591-024-03199-w · Cited by 30 · see citing works
Self-citationESMO Guidance for Reporting Oncology real-World evidence (GROW)
Castelo-Branco, L., Pellat, A., Martins-Branco, D. A., Valachis, A., Derksen, J. W. G., Suijkerbuijk, K. P. M., Dafni, U. G., Dellaporta, T., Vogel, A., Prelaj, A., et al. (2023) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2023.10.001 · Cited by 29 · see citing works
Self-citationPromise and Perils of Large Language Models for Cancer Survivorship and Supportive Care
Bitterman, D. S., Downing, A., Maués, J., & Lustberg, M. B. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.02439 · Cited by 28 · see citing works
Calibrating AI Reliance—A Physician’s Superhuman Dilemma
Patil, S. V., Myers, C. G., & Lu‐Myers, Y. (2025) · JAMA Health Forum · doi:10.1001/jamahealthforum.2025.0106 · Cited by 24 · see citing works
A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor board
Gueguen, L., Olgiati, L., Brutti-Mairesse, C., Sans, A., Texier, V. L., & Verlingue, L. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00806-y · Cited by 14 · see citing works
Self-citationEnd-To-End Clinical Trial Matching with Large Language Models
Ferber, D., Hilgers, L., Wiest, I. C., Leßmann, M.-E., Clusmann, J., Neidlinger, P., Zhu, J., Wölflein, G., Lammert, J., Tschochohei, M., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.13463 · Cited by 12 · see citing works
Self-citationLarge language models in cancer: potentials, risks, and safeguards
Zitu, M. M., Le, T. D., Duong, T., Haddadan, S., Garcia, M. A., Amorrortu, R. P., Zhao, Y., Rollison, D. E. M., & Thieu, T. (2024) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubae019 · Cited by 11 · see citing works
Technology-supported self-triage decision making
Kopka, M., Wang, S. M., Kunz, S., Schmid, C., & Feufel, M. A. (2025) · npj Health Systems · doi:10.1038/s44401-024-00008-x · Cited by 11 · see citing works
Managing Patient Use of Generative Health AI
Blumenthal, D., & Goldberg, C. B. (2024) · NEJM AI · doi:10.1056/aipc2400927 · Cited by 9 · see citing works
Use of Large Language Models in Clinical Cancer Research
Kehl, K. L. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00027 · Cited by 8 · see citing works
Patients Facing Large Language Models in Oncology: A Narrative Review
Raynaud, C., Wu, D., Levy, J., Marengo, M., & Bibault, J. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00149 · Cited by 6 · see citing works
The safety risk of information overload and bureaucracy in oncology clinical trial conduct
van Ravensteijn, S. G., Meijerink, M., Schayk, R. N.-V., Desar, I. M. E., Bol, K. F., van Herpen, C. M. L., & Verheul, H. M. W. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.01.018 · Cited by 5 · see citing works
Moving toward precision oncology centers V2.0
Doğan, S., Cournède, P.-H., Solary, É., Heard, J., Aldea, M. D., Conroy, T., Robert, C., & André, F. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.09.3115 · Cited by 5 · see citing works
Self-citationUtility of Large Language Models to Produce a Patient-Friendly Summary From Oncology Consultations
Holstead, R. G. (2024) · JCO Oncology Practice · doi:10.1200/op.24.00057 · Cited by 5 · see citing works
Large language models are good medical coders, if provided with toolsPreprint
Kwan, K. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2407.12849 · Cited by 5 · see citing works
Bibault, J., & Wu, D. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.2024.42.16_suppl.e13622 · Cited by 4 · see citing works
Regulation of Artificial Intelligence in Health Care and Biomedicine—Reply
Warraich, H. J., Tazbaz, T., & Califf, R. M. (2025) · JAMA · doi:10.1001/jama.2024.26606 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Hao Y, Holmes JM, Hobson J, et al. Retrospective comparative analysis of prostate cancer in-basket messages: responses from closed-domain large language models versus clinical teams. Mayo Clin Proc Digit Health. 2025;3(1):100198.
- Arasteh ST, Lotfinia M, Bressem K, et al. RadioRAG: factual large language models for enhanced diagnostics in radiology using dynamic retrieval augmented generation. arXiv [cs.CL]. 2024. https://doi.org/ 10.48550/arXiv.2407.15621. · DOI
- Vedadi E, Barrett D, Harris N, et al. Towards physician-centered oversight of conversational diagnostic AI. arXiv [cs.AI]. 2025. https://doi.org/10.48550/arXiv.2507.15743. · DOI
Cited by
Sorted by citations
Kremer, P., Langballe, E., Haase, I., Bamberger, J., Kuhn, S., Krusche, M., & Knitza, J. (2026) · Rheumatology International · doi:10.1007/s00296-025-06068-y · Cited by 17 · see citing works
Dehdab, R., Afat, S., Mankertz, F., Brendel, J. M., Maalouf, N., Werner, S., Brendlin, A., Herrmann, J., Nikolaou, K., Kloker, L. D., et al. (2026) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-026-06432-w · Cited by 5 · see citing works
Liu, N., Han, G., Gu, Q., Zhang, Y., & Chen, M. M. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-08769-z · Cited by 3 · see citing works
Safety and security of large language models in healthcare
Clusmann, J., Freyer, O., Ostermann, M., Ferber, D., Laleh, N. G., Hilgers, L., Kolbinger, F. R., Schneider, C. V., Downing, A., Wekenborg, M., et al. (2026) · Nature · doi:10.1038/s41586-026-10687-1 · Cited by 4 · see citing works
Self-citationABC8 Consensus: Assessment by a German Group of Experts
Ditsch, N. C., Untch, M., Fasching, P. A., Briest, S., Ettl, J., Haidinger, R., Lüftner, D., Maurer, C., Müller, V., Park-Simon, T.-W., et al. (2026) · Breast Care · doi:10.1159/000550147 · Cited by 2 · see citing works
Self-citationKoç, A., AKPINAR, O., Keskin, A., Tarhan, H. I., & Güzelsoy, M. (2026) · International Urology and Nephrology · doi:10.1007/s11255-026-05178-1 · Cited by 2 · see citing works
Varol, T. Ö., & Varol, M. (2026) · Cancer Medicine · doi:10.1002/cam4.72193 · Cited by 2 · see citing works
Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1 · see citing works
Kadirli, K., & Cantürk, A. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.112838 · Cited by 1 · see citing works
Validating large language model–assisted data extraction from clinical notes
Koevorden, J. W. V., Aben, N., Struben, V. M. D., Rollings, A., Kurban, M., Wessels, L. F. A., van der Noort, V., Burger, A. V. M., Dijk, S. W., Karssemakers, L. H. E., et al. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100718 · Cited by 1 · see citing works
Babic, B., Umihanić, Š., Osmanović, H., Selak, N., Sehic-Kozica, E., Moranjkic, L., Marijanović, I., Karaga, M., Suljevic, A. J., Umihanić, Š., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1856390 · Cited by 1 · see citing works
Artificial intelligence in oncology: empowering clinicians for responsible integration
Colliver, E., Parisini, E., Arefaine, B., Banerji, C. R. S., Verghese, G. E., & Grigoriadis, A. E. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100737 · Cited by 1 · see citing works
Dukes, D., Yost, C. D., Wang, R., Diagne, Y. F., Garr, L., Yost, M., Owaka, J., York, M., Kang, P., Sookram, J., et al. (2026) · Gynecologic Oncology · doi:10.1016/j.ygyno.2026.07.005 · Cited by 1 · see citing works
Corso, F., Zec, A., Favali, M., Ligero, M., Hilgers, L., Invernizzi, L. M., Mazzeo, L., Morales, A. M., Müller‐Franzes, G. A., Machado, I. P., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102966 · Cited by 2 · see citing works
Self-citationDonnaRosa Project: Exploring Informal Communication Practices Among Breast Cancer Specialists
Ferro, A., Atzori, F., Angiolini, C., Bortolin, M., Cortesi, L., Fabi, A., Fiorio, E., Garrone, O., Gianni, L., Giordano, M., et al. (2025) · Current Oncology · doi:10.3390/curroncol32120704
Knauer, M., Greß, J., Kather, J. N., & May, P. (2026) · doi:10.2196/preprints.93239
Self-citationInternetportale für die Hämatologie und Onkologie
Link, H. (2025) · Springer Reference Medizin · doi:10.1007/978-3-662-46764-0_368-1
Grant, C. R., Patel, S., & Azenkot, T. (2026) · Cancers · doi:10.3390/cancers18091455
Wang, J., Zhan, Y. Y., Shen, Y., Cao, F., & Ling, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1845389 · Cited by 1 · see citing works
Vijayasimha, M., & Srikanth, M. (2026) · European Journal of Plastic Surgery · doi:10.1007/s00238-026-02479-7
Malone, C., Callens, D., Forde, E., Leech, M., Cardenas, C. E., Gooding, M. J., Ryan, S. M., Thirion, P. G., Fitzpatrick, C., O’Donovan, T., et al. (2026) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2026.101210
Künstliche Intelligenz in der Molekularpathologie
Kazdal, D. N., Budczies, J., Menzel, M., Altbürger, C., Kahles, A., Ourailidis, I., Brinkmöller, M., Schirmacher, P., & Stenzinger, A. (2026) · Die Pathologie · doi:10.1007/s00292-026-01571-w
Yost, C. D., Callas, B., Halligan, P., Palumbo, P., Rawal, S. C., Leyfman, Y., & Nguyen, R. H.-T. (2026) · Cancers · doi:10.3390/cancers18121981 · Cited by 2 · see citing works
Large language models in oncology: promise, pitfalls, and the path to real-world adoption
McInerney, S., Hall, P. S., & Cresswell, K. M. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100723
Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study
Yin, X., Song, H., Moreira, P., Song, C., & Sun, Y. (2026) · Digital Health · doi:10.1177/20552076261456561
Künstliche Intelligenz im Alltag von Patient:innen
Springer, F., Nicolay, N. H., & Mehnert, A. (2026) · Forum · doi:10.1007/s12312-026-01587-6
Mohammed, Y., Kruschel, R., Alshammas, N., & Pietschmann, K. (2026) · Cureus · doi:10.7759/cureus.112361
Large Language Models for Oncology Guideline Maintenance: Prospective Case Study
Knauer, M., Greß, J., Kather, J. N., & May, P. (2026) · JMIR AI · doi:10.2196/93239
Self-citationŞimşek, M. S., Esen, E., & Koparal, M. (2026) · CRANIO® · doi:10.1080/08869634.2026.2702999
Performance of DeepSeek V3 and ChatGPT-4o in answering esophageal cancer-related questions
Yang, Q., Yi, J., Gong, A., Li, Y., Feng, Q., Fu, A., Li, J., & Zhan, Y. (2026) · Medicine · doi:10.1097/md.0000000000049896
Dehdab, R., Herrmann, J., Mankertz, F., Ghibes, P., Maalouf, N., Werner, S., Strohäker, J., Benzler, K., Zender, L., Afat, S., et al. (2026) · BMC Cancer · doi:10.1186/s12885-026-16537-7
Stifini, D., Penna, A. D., Mihaljevic, A. L., Bitzer, M., Eickhoff, C., & Capobianco, I. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03069-4
Kaddu‐Mulindwa, D., Mao, X., Duhem, C., Wilde, S. C.-D., Rauh, S., Klein, G., Jacob, F., & Servais, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1860757
Leyfman, Y., & Loaiza‐Bonilla, A. (2026) · AI in Precision Oncology · doi:10.1177/2993091x261485749
Tufekci, A., KAPLAN, E., Başgut, Ö., Durna, G., Sağır, S., & Erturhan, M. S. (2026) · BMC Urology · doi:10.21203/rs.3.rs-10701988/v1
Li, J., Zhou, P., Wang, Z., Zhang, J., Wu, Y., Hou, W., Zhang, Y., He, W., Wang, T., & Zheng, H. (2026) · Nano Energy · doi:10.1016/j.nanoen.2026.112398
Wen, Y., Chen, L., Deng, X., Lan, J., Huang, X., Liu, Y., Chen, L., & Li, L. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1977377
Hou, Y., Liu, M., Wang, Z., & Jiang, X. (2026) · Frontiers in Ophthalmology · doi:10.3389/fopht.2026.1943376
Dindelegan, G., Poupel, N. Y. F., Golea, G. I., Fărcaș, R. A., Cristea, M. G., Filip, S. I., Filip, G. G., Darii, E., Shahin, I., Ginghină, O., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15197533
Shen, J., Yuan, Y., Han, S., Duan, X., Liu, P., & Wang, Z. (2026) · Research Square · doi:10.21203/rs.3.rs-10904290/v1
León‐Figueroa, D. A., Acosta-Porzoliz, R. P. E., Cómina-Tamayo, M. J., Aguirre-Milachay, E., Vera-Ponce, V. J., & Valladares-Garrido, M. J. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106771
Zouiten, O., Azarou, L., & Belbaraka, R. (2026) · Bulletin du Cancer · doi:10.1016/j.bulcan.2026.09.006
Yue, W., & Padanilam, B. J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02605-1
AI for detection of adverse events in the real world
Jaume, G., & Kather, J. N. (2026) · Nature Cancer · doi:10.1038/s43018-026-01258-4
Self-citation
- Authors
- Wong, E. Y. T., Verlingue, L., Aldea, M., Franzoi, M. A., Umeton, R., Halabi, S., Harbeck, N., Indini, A., Prelaj, A., Romano, E., Smyth, E., Tan, I. B., Valachis, A., Vibert, J., Wiest, I. C., Yang, Y. H., Gilbert, S., Kapetanakis, G., Pentheroudakis, G., Koopman, M., & Kather, J. N.
- Affiliations
- National Cancer Centre Singapore (SG)
- Centre Léon Bérard (FR)
- Université Paris-Saclay (FR)
- Inserm (FR)
- St. Jude Children's Research Hospital (US)
- Duke University (US)
- LMU Klinikum (DE)
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- University of Oxford (GB)
- Örebro University (SE)
- Heidelberg University (DE)
- National Health Research Institutes (TW)
- University Hospital Carl Gustav Carus (DE)
- European Society for Medical Oncology (CH)
- Utrecht University (NL)
- Corresponding author
- Kather, J.N.
- ORCID
- Venue
- Annals of Oncology
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 36
- Issue
- 12
- Pages
- 1447-1457
- ISSN
- 0923-7534 (ISSN-L)
- OpenAlex
- W4415321775
- Cited by
- 44
- References
- 62
- Citations by year
- 2026: 42 · 2025: 2
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annals of Oncology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- European Society for Medical Oncology
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- large language models, clinical guidelines, institutional systems, patient education, symptom management, clinical workflow, data privacy, algorithmic bias, regulatory compliance, human oversight, clinical decision support
- MeSH terms
- Large Language Models, Europe, Humans, Language, Medical Oncology, Neoplasms, Societies, Medical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
