Use of Artificial Intelligence Chatbots for Cancer Treatment Information
Open accessAbstract
This survey study examines the performance of a large language model chatbot in providing cancer treatment recommendations that are concordant with National Comprehensive Cancer Network guidelines.
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The National Comprehensive Cancer Network
McGivney, W. T. (1998) · Cancer · doi:10.1002/(sici)1097-0142(19980515)82:10+ · Cited by 16,760 · see citing works
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083 · see citing works
The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model
Levine, D. M., Tuwani, R., Kompa, B., Varma, A., Finlayson, S. G., Mehrotra, A., & Beam, A. (2023) · doi:10.1101/2023.01.30.23285067 · Cited by 106 · see citing works
Addressing Medical Misinformation in the Patient-Clinician Relationship
Arora, V. M., Madison, S., & Simpson, L. (2020) · JAMA · doi:10.1001/jama.2020.4263 · Cited by 36 · see citing works
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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
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Self-citationLow, Y. S., Jackson, M. L., Hyde, R. J., Brown, R. E., Sanghavi, N. M., Baldwin, J. D., Pike, C. W., Muralidharan, J., Hui, G., Alexander, N., et al. (2025) · DIGITAL HEALTH · doi:10.1177/20552076251348850 · Cited by 51 · see citing works
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“REVIEW Differences between the guidelines and the chatbot’s output were frequently ascribed to unclear answers, suggesting that care should be used when utilizing chatbots to obtain treatment-related information [69].”
BackgroundMixed methods assessment of the influence of demographics on medical advice of ChatGPT
Andreadis, K., Newman, D. R., Twan, C., Shunk, A., Mann, D. M., & Stevens, E. R. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae086 · Cited by 20 · see citing works
- Authors
- Chen, S., Kann, B. H., Foote, M. B., Aerts, H. J. W. L., Savova, G. K., Mak, R. H., & Bitterman, D. S.
- Affiliations
- Harvard University (US)
- Memorial Sloan Kettering Cancer Center (US)
- Boston Children's Hospital (US)
- Corresponding author
- Bitterman, Danielle S.
- ORCID
- Venue
- JAMA Oncology
- Publisher
- American Medical Association (AMA)
- Published
- 2023-10-01
- Volume
- 9
- Issue
- 10
- Pages
- 1459
- ISSN
- 2374-2437 (ISSN-L)
- PMC
- PMC10450584
- OpenAlex
- W4386117070
- Cited by
- 251
- References
- 5
- Citations by year
- 2026: 62 · 2025: 90 · 2024: 87 · 2023: 12
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- JAMA Oncology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- American Association for Cancer Research
- AstraZeneca
- Varian Medical Systems
- ViewRay
- National Cancer Institute (P30CA008748, K08CA279922)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- large language models, chatbots, cancer treatment recommendations, NCCN guidelines, guideline adherence, artificial intelligence in oncology, clinical decision support
- MeSH terms
- Artificial Intelligence, Humans, Neoplasms
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