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Purpose <p>Artificial intelligence models like GPT-4 (OpenAI) have the potential to support clinical decision-making in oncology. This study aimed to assess the consistency between multidisciplinary tumor board (MTB) decisions and GPT-4 model predictions in cancer patient management.</p> Patients and methods <p>A cross-sectional study was conducted involving patients aged ≥ 18&#xa0;years with definite or suspicious cancer diagnoses presented at MTBs in Ankara University Hospitals, Türkiye, from February 2021 to June 2023. GPT-4 was utilized to generate treatment recommendations based on case summaries. Three independent raters evaluated the compatibility between MTB decisions and GPT-4 predictions using a 4-point Likert scale. Cases with mean compatibility scores equal to or below 2 were reviewed by two expert oncologists for appropriateness.</p> Results <p>A total of 610 patients were included. The mean compatibility score across raters was 3.59 (SD = 0.81), indicating high agreement between GPT-4 predictions and MTB decisions. Cronbach’s alpha was 0.950 (95% CI 0.935–0.960), demonstrating excellent interrater reliability. Sixty-two cases (10.2%) had mean compatibility scores below the threshold of 2. The first expert oncologist deemed GPT-4's predictions inappropriate in 8 of these cases (12.9%), while the second deemed them inappropriate in 16 cases (25.8%). Cohen’s kappa showed moderate agreement (<i>κ</i> = 0.50, 95% CI 0.25–0.75, <i>p</i> &lt; 0.001). Discrepancies were often due to rare cases lacking guideline information or misunderstandings of case presentations.</p> Conclusion <p>GPT-4 exhibited high compatibility with MTB decisions in cancer patient management, suggesting its potential as a supportive tool in clinical oncology. However, limitations exist, especially in rare or complex cases.</p>

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Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月25日

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  • Efe Cem Erdat,
  • Merih Yalçıner,
  • Mehmet Berk Örüncü,
  • Yüksel Ürün,
  • Filiz Çay Şenler

摘要

Purpose

Artificial intelligence models like GPT-4 (OpenAI) have the potential to support clinical decision-making in oncology. This study aimed to assess the consistency between multidisciplinary tumor board (MTB) decisions and GPT-4 model predictions in cancer patient management.

Patients and methods

A cross-sectional study was conducted involving patients aged ≥ 18 years with definite or suspicious cancer diagnoses presented at MTBs in Ankara University Hospitals, Türkiye, from February 2021 to June 2023. GPT-4 was utilized to generate treatment recommendations based on case summaries. Three independent raters evaluated the compatibility between MTB decisions and GPT-4 predictions using a 4-point Likert scale. Cases with mean compatibility scores equal to or below 2 were reviewed by two expert oncologists for appropriateness.

Results

A total of 610 patients were included. The mean compatibility score across raters was 3.59 (SD = 0.81), indicating high agreement between GPT-4 predictions and MTB decisions. Cronbach’s alpha was 0.950 (95% CI 0.935–0.960), demonstrating excellent interrater reliability. Sixty-two cases (10.2%) had mean compatibility scores below the threshold of 2. The first expert oncologist deemed GPT-4's predictions inappropriate in 8 of these cases (12.9%), while the second deemed them inappropriate in 16 cases (25.8%). Cohen’s kappa showed moderate agreement (κ = 0.50, 95% CI 0.25–0.75, p < 0.001). Discrepancies were often due to rare cases lacking guideline information or misunderstandings of case presentations.

Conclusion

GPT-4 exhibited high compatibility with MTB decisions in cancer patient management, suggesting its potential as a supportive tool in clinical oncology. However, limitations exist, especially in rare or complex cases.