Large language model integrations in cancer decision-making: a systematic review and meta-analysis
Open accessAbstract
Large Language Models (LLMs) are increasingly used to support cancer patients and clinicians in decision-making. This systematic review investigates how LLMs are integrated into oncology and evaluated by researchers. We conducted a comprehensive search across PubMed, Web of Science, Scopus, and the ACM Digital Library through May 2024, identifying 56 studies covering 15 cancer types. The meta-analysis results suggested that LLMs were commonly used to summarize, translate, and communicate clinical information, but performance varied: the average overall accuracy was 76.2%, with average diagnostic accuracy lower at 67.4%, revealing gaps in the clinical readiness of this technology. Most evaluations relied heavily on quantitative datasets and automated methods without human graders, emphasizing “accuracy” and “appropriateness” while rarely addressing “safety”, “harm”, or “clarity”. Current limitations for LLMs in cancer decision-making, such as limited domain knowledge and dependence on human oversight, demonstrate the need for open datasets and standardized evaluations to improve reliability.
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Related reviews have examined health care chatbots and LLMs for cancer decision-making.
“Related reviews have examined health care chatbots [Laymouna M, Ma Y, Lessard D, Schuster T, Engler K, Lebouché B. Roles, users, benefits, and limitations of chatbots in health care: rapid review. J Med Internet Res. Jul 23, 2024;26:e56930. [FREE Full text] [CrossRef] [Medline]13] and LLMs for cancer decision-making”
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Hao, Y., Holmes, J., Waddle, M. R., Davis, B. J., Yu, N. Y., Vickers, K. S., Preston, H. R., Margolin, D., Löckenhoff, C. E., Vashistha, A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02166-0 · Cited by 29 · see citing works
Self-citationBirsin, Z., Jeral, S., Cebeci, S., Çerme, E., Aliyev, V., Günaltılı, M., Abbasov, H., Çiçek, E., Demirci, N. S., & Alan, Ö. (2025) · Future Oncology · doi:10.1080/14796694.2025.2610463 · Cited by 5 · see citing works
Benchmarking GPT-5 in radiation oncology: measurable gains, but persistent need for expert oversight
Dinç, U., Sarkar, J., Schubert, P., Semrau, S., Weißmann, T., Karius, A., Brand, J., Axer, B., Gomaa, A., Stephan, P., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1695468 · Cited by 5 · see citing works
Lamberti, G., Panzuto, F., Massironi, S., Cives, M., Salvia, A. L., Spada, F., Faggiano, A., Pusceddu, S., Albertelli, M., Tafuto, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02274-x · Cited by 4 · see citing works
Large language models exhibit stigmatizing behaviour in contextual judgements of health conditions
Wang, X., Zhou, Y., & Zhou, G. (2026) · Nature Health · doi:10.1038/s44360-026-00164-4 · Cited by 4 · see citing works
Tini, P., Novi, F., Donnini, F., Perrella, A., Bagnacci, G., Mazzei, M. A., & Minniti, G. C. (2025) · Journal of Neuro-Oncology · doi:10.1007/s11060-025-05254-z · Cited by 4 · see citing works
Yu, Y. Q., Li, L., & Li, Y. (2025) · medRxiv · doi:10.1101/2025.10.17.25338266 · Cited by 5 · see citing works
Wu, J., Tang, X., Zheng, Q., Gu, X., Ma, L. M., Xian, J., Mao, H. Z., Gan, J., & Ji, G. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1695749 · Cited by 3 · see citing works
Yap, W., Cheng, S.-C., Lin, C.-H., Hsiao, I., Tsai, T., Yap, W.-L., Chen, W. P.-Y., Lin, C., & Huang, S.-M. (2025) · Bioengineering · doi:10.3390/bioengineering12121345 · Cited by 3 · see citing works
Retrieval-Augmented Generation in Oncology: Promises, Pitfalls, and Early Applications
Thaker, N. G., Liu, W., Waddle, M., Showalter, T. N., Mastroleo, F., Luh, J. Y., Levitt, C., Ning, M., Loaiza‐Bonilla, A., & Hong, J. C. (2026) · AI in Precision Oncology · doi:10.1177/2993091x261446348 · Cited by 3 · see citing works
Self-citationStructured reasoning failures compromise LLM interpretation of clinical oncology notes
Kenaston, M. W., Ayub, U., Parmar, M., Kumar, P., Anjum, M. U., Naqvi, S. A. A., Chaudhuri, A. A., Zakharia, Y., Heath, E. I., Bekaii-Saab, T. S., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02951-5 · Cited by 2 · see citing works
AI assistance in tumor multidisciplinary teams
Foppen, R. J. G., Morkūnas, M., Traverso, A., & Dienstmann, R. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100684 · Cited by 2 · see citing works
Koç, 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
Karni, J. E., Simon, C., & Hack, S. (2026) · Digital Health · doi:10.1177/20552076261450749 · Cited by 2 · see citing works
Global trends and emerging frontiers of large language models in cancer research
Tian, D., Xie, Z., Hu, Z., Yang, Z., Tian, H., Chen, Y., Zhao, H., Du, S., Wang, F., Zhang, L., et al. (2026) · Digital Health · doi:10.1177/20552076261458966 · Cited by 2 · see citing works
Alkadhi, O. H. (2026) · International Orthodontics · doi:10.1016/j.ortho.2026.101212 · Cited by 2 · see citing works
Zhou, Z., Wang, M., Li, J., Wang, Z., & Lv, H. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03723-7 · Cited by 2 · see citing works
Qi, X. S., Wang, Y., Yang, X., Ren, L., Liu, W., Benedict, S., Xiao, Y., Xing, L., & Naqa, I. M. E. (2026) · Medical Physics · doi:10.1002/mp.70616 · Cited by 2 · see citing works
Self-citationThe potential of large language models in the field of infertility: a systematic review
Li, W., Ur-Rehman, A., Ge, M., Shen, L.-T., Peng, X.-Y., Zhong, K., Feng, R., Gao, S., Hu, F., Jia, Y., et al. (2025) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-025-03679-z · Cited by 1 · see citing works
From Prompt Engineering to Context Engineering: Why Precision Oncology Needs Context
Thaker, N. G., & Juneja, S. (2025) · AI in Precision Oncology · doi:10.1177/2993091x251388462 · Cited by 1 · see citing works
KAHYAOGLU, S., KAYGISIZ, A., Alatli, İ., Boyaci, A. I., Aray, E., Tulgar, S., & Balcı, D. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15020716 · Cited by 1 · see citing works
Advancing the science of qualitative patient preference assessment using large language models
Grover, T., Krebs, E., Weymann, D., Ehman, M., & Regier, D. A. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001263 · Cited by 1 · see citing works
Elkefi, S., Scheinmann, R., Hao, D., Bhar, S., Tounsi, A., & Feiner, S. (2026) · BMC Artificial Intelligence · doi:10.1186/s44398-026-00024-x · Cited by 1 · see citing works
May, P., Brookman-May, S. D., Garrahy, E., & von Büren, J. (2026) · Journal of Medical Internet Research · doi:10.2196/92642 · Cited by 1 · see citing works
Kayira, A. B., Elyazori, H. R. A., Lybarger, K., Walter, F. M., Chelala, C., & Funston, G. (2026) · JMIR AI · doi:10.2196/73481 · Cited by 1 · see citing works
Liu, S., Wang, H., & He, L. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1848084 · Cited by 1 · see citing works
Chow, N.-H., Chang, H.-C., Chen, H.-K., Lin, C.-Y., Liu, Y.-L., Tseng, P., Shiu, L.-J., Chu, Y.-W., Chung, P., & Chang, K.-P. (2026) · Diagnostics · doi:10.3390/diagnostics16111644 · Cited by 1 · see citing works
Large language models instantiate evolutionarily robust strategies of cooperation
Pal, S., Mallela, A., Pracher, L., Wei, C., Fu, F., Schnell, S. D., & Nowak, M. A. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag210 · Cited by 1 · see citing works
Jeong, Y., Cha, H., & Suh, E. E. (2026) · JMIR Cancer · doi:10.2196/96241 · 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
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
Menz, B. D., Scarfo, N. L., Cornelisse, E., Shahnam, A., Chin-Yee, B. H., Rickard, C., Haseloff, M., Lau, A. Y. S., Ugalde, A., Paterson, C. C., et al. (2026) · European Journal of Cancer · doi:10.1016/j.ejca.2026.116990 · Cited by 1 · see citing works
Shen, Z., Shi, S., Mao, L., Li, C., & Xie, J. (2026) · Journal of Medical Internet Research · doi:10.2196/105599
Tsitsi, T., Polychronis, G., Papoui, E., Cloconi, C., Michail, K., & Charalambous, A. (2026) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2026.100789
Ethical Hazards of Large Language Models in Primary Care: A Clinician-Focused Update
Lu, M. M., Gillette, J. J., & Heston, T. F. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1012864
Knauer, M., Greß, J., Kather, J. N., & May, P. (2026) · doi:10.2196/preprints.93239
May, P., Brookman-May, S., Garrahy, E., & von Büren, J. (2026) · doi:10.2196/preprints.92642
Large Language Models in Healthcare: A Meta-Analysis for Clinical Decision-Making
Paikrao, P. D., Bhalke, D. G., Wagh, M., Chougule, S., Chavan, S. S., Deshpande, C., & Sayyad, F. A. (2025) · International Conference on Artificial Intelligence for Computing, Astronomy and Renewable Energy (AICARE) · doi:10.1109/aicare66005.2025.11402661
Quantitative Synthesis of Large Language Model Performance in Medical Reasoning Tasks
Paikrao, P. D., Wagh, M., Chavan, S. S., Chougule, S., Wattamwar, R. V., Sayyad, F. A., Bhalke, D. G., & Deshpande, C. (2026) · Biennial International Conference on Nascent Technologies in Engineering (ICNTE) · doi:10.1109/icnte66387.2026.11437404
Xu, L., Zhao, W., Qin, Y., & Wang, J. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02372-7
Jeong, Y., Cha, H., & Suh, E. (2026) · doi:10.2196/preprints.96241
GeneDrug: A Retrieval Platform for Analyzing Gene-Drug Relations in Cancer
Su, L., Xie, X., Qian, W., Shao, M., He, Y., & Zhang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0378-9_50
Künstliche Intelligenz in Gesundheitsinformationen
Köster, M.-J., Ruetters, D., & Rudnick, T. (2026) · Forum · doi:10.1007/s12312-026-01591-w
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
Wang, Y., Yao, J., Tian, J., Wang, Y., & Yang, Y. (2026) · JMIR Research Protocols · doi:10.2196/88163
Stalp, J. L., Schneider, J. A., Krause, A., STEINKASSERER, L., Hachenberg, J., Denecke, A., Hillemanns, P., & Wolff, D. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1896184
Nakamura, S., Karayama, M., Inoue, Y., Yasui, H., Hozumi, H., Suzuki, Y., Furuhashi, K., Enomoto, N., Inui, N., Suda, T., et al. (2026) · International Journal of Lung Cancer · doi:10.1016/j.ijlcan.2026.100058
Correct but Incomplete: Limitations of AI-Assisted Decision Support in Rectal Cancer
Meyer, R. J., Bresler, T. E., Makaryan, T., Gheissari, R., Mauch, A., & Fujita, M. (2026) · The American Surgeon · doi:10.1177/00031348261471501
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
Holmes, J., Mastroleo, F., Borras-Osorio, M., Seetamsetty, S., Shiraishi, S., Fatyga, M., Boughey, J. C., Thiels, C. A., Breen, W. G., Ma, D. J., et al. (2026) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2026.101261
Self-citationZhao, W., Chen, L., Pu, W., Liu, L., Tan, X., Li, S., Qiu, X., Fan, J., Zhu, M., & Wang, Z. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134108
Artificial intelligence for perioperative precision in surgical oncology
Pan, G. (2026) · Frontiers in Surgery · doi:10.3389/fsurg.2026.1865406
Thanabalachandran, L., Ali, D., Newman-Simmons, A., Norman, C., Jafari, D., Grahovac, T., Pollett, A. F., Huang, W., Chen, L., Kassam, S. N., et al. (2026) · Current Oncology · doi:10.3390/curroncol33090540
Mathematical Oncology: Models, Methods, and Clinical Applications
Zigman, P. (2026) · Tatra Mountains Mathematical Publications · doi:10.2478/tmmp-2026-0011
Wang, Y., Ornelas, M. D., Studenski, M. T., Bossart, E. L., Nejad‐Davarani, S. P., & Yang, Y. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001740
Alemdar, M. S., Yılmazer, Y. Ç., Günbayı, İ., & Mutlu, H. (2026) · Current Oncology · doi:10.3390/curroncol33090563
Tian, Q., Wang, J., Gu, J., Yu, J., Ji, Y., & Huo, J. (2026) · doi:10.2196/preprints.112145
Performance of large language models in ambiguous treatment scenarios of early breast cancer
Oseledchyk, A., Weber, W. P., & Kasenda, B. (2026) · The Breast · doi:10.1016/j.breast.2026.104932
Latent-CURE: Interpretable Breast Cancer Diagnosis via Dual-Asymmetric Chain-of-Thought
Zhao, W., Tan, X., Gan, L., Liu, L., & Qiu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38098-2_35
AI in Proton Therapy Treatment Planning: A Narrative Review
Ding, Y., Feng, H., Bues, M., Fatyga, M., Liu, T., Zhang, W., Whitaker, T. J., Lin, H., Lee, N. Y., Simone, C. B., et al. (2026) · International Journal of Particle Therapy · doi:10.1016/j.ijpt.2026.101965
Self-citationPenedo, F. J., Fleszar‐Pavlović, S. E., Natori, A., Wang, A., Stathias, V., Bonnell, J. T., Esquives, B. S. N., Crane, T. E., MacIntyre, J., Calfa, C. J., et al. (2026) · Translational Behavioral Medicine · doi:10.1093/tbm/ibag063
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
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Holmes, J., Hao, Y., Borras-Osorio, M., Mastroleo, F., Brufau, S. R., Carducci, V., Abel, K. M., Routman, D. M., Foong, A., Muller, O. M., et al. (2026) · Meta-Radiology · doi:10.1016/j.metrad.2026.100245
Self-citationShen, J., Yuan, Y., Han, S., Duan, X., Liu, P., & Wang, Z. (2026) · Research Square · doi:10.21203/rs.3.rs-10904290/v1
Popescu, A., Popescu, M. J., Mahler, B., Olteanu, R., & Clem, E. (2026) · Current Oncology · doi:10.3390/curroncol33100640
Barbaric, A., Benseler, S. M., Lalloo, C., Liu, X. C., Mariakakis, A. T., Phạm, Q., Vastert, S. J., Yeung, R. S. M., Cafazzo, J. A., & Swart, J. F. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001655
Şanlı, A., Göçer, S., & Güngör, H. S. (2026) · Yeni Üroloji Dergisi · doi:10.33719/nju1968495
- Authors
- Hao, Y., Qiu, Z., Holmes, J., Löckenhoff, C. E., Liu, W., Ghassemi, M., & Kalantari, S.
- Affiliations
- Mayo Clinic (US)
- Cornell University (US)
- Mayo Clinic Hospital (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Hao, Yuexing
- Liu, Wei
- Venue
- npj Digital Medicine
- Published
- 2025-07-17
- Volume
- 8
- Issue
- 1
- Pages
- 450-450
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC12271406
- OpenAlex
- W4412490346
- Cited by
- 67
- References
- 82
- Citations by year
- 2026: 55 · 2025: 12
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- American Psychological Foundation
- National Cancer Institute (R01CA280134)
- Eric & Wendy Schmidt Fund for AI Research and Innovation
- Fred C. and Katherine B. Anderson Foundation
- Kemper Marley Foundation
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- large language models, clinical decision support, LLM evaluation, diagnostic accuracy, clinical translation, human oversight, standardized assessment
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 ›
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