Integrating Evidence-Based Medicine and Artificial Intelligence in Ophthalmology Education: A Teaching Reform Study

Abstract

Purpose: This research sets out to explore the practical application value of an innovative teaching mode that organically combines evidence-based medicine (EBM) with artificial intelligence (AI), and analyze its application effect on undergraduate medical students during their ophthalmology internship. Methods: This research team independently built a multi-functional online teaching platform integrating EBM and AI technologies, which incorporates specialized evidence resource libraries, AI auxiliary diagnosis tools, and simulated clinical case modules. A total of 30 fifth-year clinical undergraduates receiving ophthalmology internship training were enrolled as research subjects and randomly assigned into two groups with 15 participants each. The experimental group adopted the newly developed EBM-AI integrated teaching method, while the control group received the conventional offline teaching mode. Multiple indicators were adopted to evaluate the teaching effect, covering students’ professional theoretical reserves, EBM and AI-related comprehensive abilities, clinical decision-making level, autonomous learning enthusiasm, and subjective evaluation of teaching quality. Results: Before the intervention, there was no statistical difference in the basic ophthalmology knowledge reserve between the two groups (P > 0.05). After the implementation of different teaching schemes, the experimental group outperformed the control group in all evaluation indicators. To be specific, the experimental group obtained higher scores in overall theoretical assessment (86.12 ± 4.05 vs. 74.93 ± 5.62, P < 0.001) and EBM/AI specialized knowledge test (36.25 ± 2.31 vs. 26.80 ± 3.17, P < 0.001). Meanwhile, the group also achieved better results in the Script Concordance Test (SCT) for clinical decision-making ability (81.60 ± 5.24 vs. 63.87 ± 6.08, P < 0.001). In terms of learning initiative, the weekly self-study duration of students in the experimental group was obviously longer than that of the control group (4.02 ± 0.71 vs. 1.68 ± 0.44, P < 0.001). Additionally, the student satisfaction rate of the new teaching model reached 93.3%, which was notably higher than 73.3% of the traditional teaching group (P = 0.031). Conclusion: The integrated teaching model combining EBM and AI can effectively help interns consolidate EBM and AI professional knowledge, optimize clinical case analysis and decision-making capabilities, and stimulate their enthusiasm for independent learning. Importantly, the new teaching method will not affect students’ mastery of basic ophthalmology knowledge. This optimized teaching scheme can serve as a reliable reference for promoting the innovative reform of modern ophthalmology higher education.