High Concordance between GPT-4o and Multidisciplinary Tumor Board Decisions in Breast Cancer: A Retrospective Decision Support Analysis
Open accessAbstract
Large language models (LLMs) such as ChatGPT have gained attention for their potential to assist clinical decision-making in oncology. However, real-world validation of these models against multidisciplinary tumor board (MTB) recommendations-particularly in breast cancer treatment-remains limited. This retrospective study assessed the concordance between GPT-4o and the decisions of a breast cancer MTB over a six-month period. Thirty-three patients were included. Structured clinical data were entered into GPT-4o using standardized prompts, and treatment plans were generated in two independent sessions per case. Seven therapeutic domains were evaluated: surgery, radiotherapy, hormonal therapy, neoadjuvant therapy, adjuvant therapy, genetic counseling/testing, and dual HER2-targeted therapy. Two blinded reviewers scored concordance using a 5-point Likert scale. Inter-rater reliability and classification metrics were calculated. GPT-4o generated consistent recommendations across both sessions for all patients. Full concordance (5/5) with MTB decisions was observed in 31 of 33 cases (93.9%), while partial concordance (4/5) occurred in 2 cases (6.1%) due to differences regarding genetic counseling. Inter-rater agreement was perfect (Cohen's kappa = 1.00), and the mean concordance score was 4.94 out of 5. The model achieved an overall accuracy of 93.9%, precision of 93.9%, recall of 100%, and F1 score of 96.8%. GPT-4o demonstrated a high level of agreement with expert multidisciplinary decisions in breast cancer care when provided with structured clinical input. These findings support its potential as a reproducible, guideline-consistent decision-support tool in oncology workflows.
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Büyükceran et al. achieved 93.9% concordance in 33 patients using highly structured inputs.
“Büyükceran et al. achieved 93.9% concordance in 33 patients using highly structured inputs [21]”
Our concordance rates are consistent with findings from studies in similarly complex clinical settings.
“Our concordance rates are consistent with findings from studies in similarly complex clinical settings.”
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- Authors
- Büyükceran, E. U., Seyfettin, A., Babatürk, A., Özkan, M. B., Çolak, D., Ünal, İ., Kaymaz, E., Ergün, E., Emer, M. Ö., & Mersin, H. H.
- Affiliations
- Güven Hospital (TR)
- Corresponding author
- Büyükceran, Emre Utkan
- Çolak, Dilşen
- Ünal, İlhami
- ORCID
- Venue
- Journal of Medical Systems
- Published
- 2025-12-08
- Volume
- 49
- Issue
- 1
- Pages
- 179-179
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W4417153033
- Cited by
- 6
- References
- 21
- Citations by year
- 2026: 6
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Medical Systems (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- 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
- GPT-4o, large language models, multidisciplinary tumor board, breast cancer, clinical decision support, treatment planning, model validation, HER2-targeted therapy, genetic counseling, neoadjuvant therapy, adjuvant therapy, hormone therapy, radiotherapy
- MeSH terms
- Clinical Decision-Making, Adult, Aged, Breast Neoplasms, Decision Support Techniques, Female, Humans, Middle Aged, Retrospective Studies, Reproducibility of Results, Decision Support Systems, Clinical
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