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Cited by 12Artificial Intelligence in Healthcare and EducationAI in cancer detectionArtificial Intelligence Applications

Chat GPT Performance in Multi-Disciplinary Boards—Should AI Be a Member of Cancer Boards?

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Dogan, Ibrahim, Bartin, Mehmet Kadir, Sonmez, Ezgi, Seyran, Erdogan, Bozkurt, Halil Alper, Yuksek, Mehmet, Serbes, Ezgi Dicle, Zalova, Gunel +1 more·2025·Healthcare
DOI 10.3390/healthcare13182254PMC PMC12469382
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Abstract

Background: Multidisciplinary Tumor Councils (MDTs) are vital platforms that provide tailored treatment plans for cancer patients by combining expertise from various medical disciplines. Recently, Artificial Intelligence (AI) tools have been investigated as decision-support systems within these councils. Methods: In this prospective study, the compatibility of AI (ChatGPT-4.0) with MDT decisions was evaluated in 100 cancer patients presented to the tumor council between November 2024 and January 2025. AI-generated treatment recommendations based on anonymized, detailed clinical summaries were compared with real-time MDT decisions. Cohen’s Kappa and Spearman correlation tests were used for statistical analysis. Results: Neoadjuvant treatment (45%) and surgery (36%) were the most frequent MDT decisions. AI recommended surgery (39%) and neoadjuvant treatment (37%) most frequently. A high concordance rate of 76.4% was observed between AI and MDT decisions (κ = 0.764 [95% CI; 0.658–0.870] p < 0.001, ρ = 0.810 [95% CI; 0.729–0.868], p < 0.001). Most inconsistencies arose in cases requiring individualized decisions, indicating AI’s current limitations in incorporating contextual clinical judgment. Conclusion: AI demonstrates substantial agreement with MDT decisions, particularly in cases adhering to standardized oncological guidelines. However, for AI integration into clinical workflows, it must evolve to interpret real-time patient data and function transparently within ethical and legal frameworks.

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Cited by

Sorted by citations

Authors
Dogan, I., Bartin, M. K., Sonmez, E., Seyran, E., Bozkurt, H. A., Yuksek, M., Serbes, E. D., Zalova, G., & Celik, S.
Affiliations
  • Department of General Surgery, Van Training and Research Hospital, University of Health Sciences, 65300 Van, Türkiye
  • Department of Medical Oncology, Van Training and Research Hospital, University of Health Sciences, 65300 Van, Türkiye
  • Sağlık Bilimleri Üniversitesi (TR)
  • Department of Radiology, Van Training and Research Hospital, University of Health Sciences, 65300 Van, Türkiye
ORCID
Venue
Healthcare
Publisher
MDPI AG
Published
2025-09-09
Volume
13
Issue
18
Pages
2254
ISSN
2227-9032 (ISSN-L)
DOI
10.3390/healthcare13182254
PMC
PMC12469382
OpenAlex
W4414213044
Cited by
12
References
0
Citations by year
2026: 11 · 2025: 1
FWCI
2.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Healthcare (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
ChatGPT, artificial intelligence in oncology, multidisciplinary tumor board, cancer treatment decision-making, neoadjuvant therapy, surgical decision-making, clinical decision support

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