ArticleJournal of medical systems2025
High Concordance Between GPT-4o and Multidisciplinary Tumor Board Decisions in Breast Cancer: A Retrospective Decision Support Analysis.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study.Journal of clinical medicine · 2026Article
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.