Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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.
Institution: Ankara University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,690
OpenAlex ID: W4408884145