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Published on: May 17, 2019
Evaluating the potential of ChatGPT for patient identification in clinical breast cancer trials
Annika Krückel1,2,3, Peter A Fasching1,2,3, Oliver Schleicher1,2,3
1Department of Gynecology and Obstetrics, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
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.
Insights
Artificial intelligence (AI) like ChatGPT-4.0 shows potential but is not yet ready for standalone use in identifying breast cancer patients for clinical trials. Multiprofessional teams remain crucial for accurate patient recruitment.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence
Background:
- Clinical trial recruitment for complex oncological treatments faces significant challenges.
- Identifying suitable participants is critical for trial success and timely research advancement.
- Artificial intelligence (AI) offers a potential solution to streamline patient identification processes.
Purpose of the Study:
- To evaluate the efficacy of ChatGPT-4.0 in identifying eligible breast cancer patients for clinical trials.
- To compare AI-driven patient identification with clinician assessments using real-world tumor board data.
Main Methods:
- ChatGPT-4.0 was trained on fictitious breast cancer trial protocols.
- Anonymized tumor board data from 124 patients were analyzed by AI and a clinician control group.
- Performance was benchmarked against an expert-validated standard, calculating sensitivity and specificity.
Main Results:
- ChatGPT-4.0 demonstrated high specificity in excluding ineligible patients but varied sensitivity, particularly for neoadjuvant trials.
- Clinicians showed better, though heterogeneous, performance compared to the AI.
- Team-based assessments identified nearly all eligible patients, highlighting collaborative value.
Conclusions:
- Current AI models like ChatGPT-4.0 are not yet suitable as standalone tools for breast cancer clinical trial patient identification.
- Multiprofessional team involvement is essential for accurate and efficient patient recruitment.
- Future AI utility may improve with model refinement and larger, detailed datasets.
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