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Evaluating ChatGPT as an Adjunct for the Multidisciplinary Tumor Board Decision–Making in Primary Breast Cancer Cases
Open accessPublished version of a preprint
Abstract
BACKGROUND: As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer? MATERIAL AND METHODS: We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated. RESULTS: Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy "hormonal treatment". CONCLUSIONS: Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.
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“In the realm of pharmaceutical research, ChatGPT can contribute by sifting through vast volumes of scientific literature, identifying potential drug candidates, and suggesting innovative research directions.”
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“It can assist in drug discovery, research proposal writing, and summarising complex medical research, expediting the research process [11,24,34,35,36,37,38].”
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Cites
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“Additionally, the ChatGPT can pass the United States Medical Licensing Exam [10].”
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“There are already tools calculating the probability of the BRCA Mutation in a particular patient as KOHBRA, BRCAPRO, or Myriad [2].”
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Research works printed but not linked: 1
- Lukac S, Friedl TWP, Leinert E (2023) Age of the patient as a prognosis and therapy-relevant factor in early breast cancer. University of Ulm, Ulm
Cited by
Sorted by citations
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
Younis, H. A., Eisa, T. A. E., Nasser, M., Sahib, T. M., Noor, A. A., Alyasiri, O. M., Salisu, S., Hayder, I. M., & Younis, H. A. (2024) · Diagnostics · doi:10.3390/diagnostics14010109 · Cited by 242
“Misidentification: Incorrectly identifying treatment needs, such as Her2 1+ and 2+ patients.”
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Large language model use in clinical oncology
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“Questioning procedure continued “ Prompt Engineering ” PromptEngineering(PE)wasreportedin9outof34studies 15,17,20,21,23,27,33,34,41 .”
BackgroundMethodGriewing, S., Gremke, N., Wagner, U. A. G., Lingenfelder, M., Kühn, S., & Boekhoff, J. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13101502 · Cited by 78
“[22,23].”
BackgroundMethodResultInfluential
Metadata
- Authors
- Lukac, S., Dayan, D., Fink, V., Leinert, E., Hartkopf, A., Veselinovic, K., Janni, W., Rack, B., Pfister, K., Heitmeir, B., & Ebner, F.
- Affiliations
- University Hospital Ulm (DE)
- Corresponding author
- Lukac, Stefan
- ORCID
- Venue
- Archives of Gynecology and Obstetrics
- Published
- 2023-07-17
- Volume
- 308
- Issue
- 6
- Pages
- 1831-1844
- ISSN
- 0003-9128 (ISSN-L)
- 1432-0711
- PMC
- PMC10579162
- OpenAlex
- W4384558920
- Cited by
- 130
- References
- 23
- Citations by year
- 2026: 32 · 2025: 44 · 2024: 45 · 2023: 9
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Archives of Gynecology and Obstetrics (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Universitätsklinikum Ulm
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- ChatGPT, large language models, multidisciplinary tumor board, primary breast cancer, early breast cancer, treatment decision-making, clinical implementation of AI, HER2-positive breast cancer, endocrine therapy, personalized treatment
- MeSH terms
- Aged, Antibodies, Artificial Intelligence, Breast Neoplasms, Female, Humans, Oncogenes, Pilot Projects
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