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Introducing AI as members of script concordance test expert reference panel: A comparative analysis.
Moataz A Sallam1, Enjy Abouzeid2,3
1Ophthalmology Department, Suez Canal University, Ismailia, Egypt.
Medical Teacher
|March 8, 2025
Summary
Artificial intelligence (AI) models can assist in creating Script Concordance Tests (SCTs) for clinical reasoning training. While not replacing human experts, AI enhances efficiency and helps bridge the performance gap for medical students.
Area of Science:
- Medical Education
- Artificial Intelligence in Healthcare
- Clinical Reasoning Assessment
Background:
- The Script Concordance Test (SCT) is a valuable tool for assessing clinical reasoning in professional development.
- Expert Reference Panel (ERP) burnout is a significant challenge in SCT creation.
- Exploring AI, specifically ChatGPT, as an alternative for ERP membership is crucial for efficiency.
Purpose of the Study:
- To enhance the efficiency of SCT creation using AI models.
- To maintain the educational quality of SCTs with AI involvement.
- To evaluate the effectiveness of different AI models as reference panels compared to human experts.
Main Methods:
- A quasi-experimental comparative design was used, involving undergraduate medical students and faculty in an Ophthalmology clerkship.
- Two groups were established: a Traditional ERP (15 human experts) and an AI-Generated ERP (using ChatGPT and o1 preview).
- AI panels were designed to mirror diverse clinical opinions based on varying experience levels.
Main Results:
- Human experts generally achieved the highest mean scores on SCT vignettes.
- AI models (ChatGPT-4 and o1) produced slightly lower scores, with o1 scores being closer to expert performance.
- Both human experts and AI models demonstrated high consistency and reliability in their ratings.
Conclusions:
- AI models show promise in augmenting the creation of clinical reasoning assessments.
- AI can be utilized for training medical students and improving their reasoning skills.
- AI tools can help reduce the disparity between student and expert performance levels in clinical reasoning.
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