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Ophthalmologie ; 121(7): 554-564, 2024 Jul.
Article in German | MEDLINE | ID: mdl-38801461

ABSTRACT

PURPOSE: In recent years artificial intelligence (AI), as a new segment of computer science, has also become increasingly more important in medicine. The aim of this project was to investigate whether the current version of ChatGPT (ChatGPT 4.0) is able to answer open questions that could be asked in the context of a German board examination in ophthalmology. METHODS: After excluding image-based questions, 10 questions from 15 different chapters/topics were selected from the textbook 1000 questions in ophthalmology (1000 Fragen Augenheilkunde 2nd edition, 2014). ChatGPT was instructed by means of a so-called prompt to assume the role of a board certified ophthalmologist and to concentrate on the essentials when answering. A human expert with considerable expertise in the respective topic, evaluated the answers regarding their correctness, relevance and internal coherence. Additionally, the overall performance was rated by school grades and assessed whether the answers would have been sufficient to pass the ophthalmology board examination. RESULTS: The ChatGPT would have passed the board examination in 12 out of 15 topics. The overall performance, however, was limited with only 53.3% completely correct answers. While the correctness of the results in the different topics was highly variable (uveitis and lens/cataract 100%; optics and refraction 20%), the answers always had a high thematic fit (70%) and internal coherence (71%). CONCLUSION: The fact that ChatGPT 4.0 would have passed the specialist examination in 12 out of 15 topics is remarkable considering the fact that this AI was not specifically trained for medical questions; however, there is a considerable performance variability between the topics, with some serious shortcomings that currently rule out its safe use in clinical practice.


Subject(s)
Educational Measurement , Ophthalmology , Specialty Boards , Ophthalmology/education , Educational Measurement/methods , Educational Measurement/standards , Germany , Humans , Clinical Competence/standards , Certification , Artificial Intelligence
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