ArticleDigital health
Evaluating the potential of ChatGPT for patient identification in clinical breast cancer trials.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
Objective: Growing complexity of oncological treatment is reflected in the requirements for current clinical trials, challenging clinical sites with recruiting suitable participants. This cross-sectional study evaluates the potential of artificial intelligence (AI), based on the example of ChatGPT-4.0, in identifying suitable study participants among patients with breast cancer, utilizing real-world tumor board data. Methods: ChatGPT-4.0 was trained on six fictitious study protocols for patients with breast cancer, mimicking real-world clinical trial scenarios. Anonymized data from 124 tumor board registrations from January 2024 were submitted to the AI to determine eligibility for study participation. A clinician control group also assessed the patients' eligibility. The evaluations of ChatGPT-4.0 and the medical professionals were benchmarked against an expert-validated reference standard. Sensitivity and specificity were calculated for the AI as well as for each member of the control group. Results: Overall, among the 124 tumor board registrations, 19 patients met eligibility criteria for at least one study. Both AI and clinicians reliably excluded ineligible patients (high specificity), but sensitivity varied. ChatGPT-4.0 proved especially ineffective at screening for neoadjuvant trials, whereas medical professionals showed better, but heterogeneous performance. Team-based assessment identified nearly all eligible patients, underscoring the value of collaborative decision making. Conclusion: While model performance was limited by simplified input data and a small single-center cohort, the results suggest that ChatGPT-4.0, in its current form, is not yet suitable as a stand-alone tool for patient identification in clinical breast cancer trials. To ensure accurate and efficient recruitment, the involvement of a multiprofessional team remains essential. Ongoing model refinement and access to larger, more detailed datasets may enhance the future utility of AI systems in clinical trial screening.
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