Comparison of medical history documentation efficiency and quality based on GPT-4o: a study on the comparison between residents and artificial intelligence
BackgroundAs medical technology advances, physicians' responsibilities in clinical practice continue to increase, with medical history documentation becoming an essential component. Artificial Intelligence (AI) technologies, particularly advances in Natural Language Processing (NLP), have intro...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1545730/full |
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| Summary: | BackgroundAs medical technology advances, physicians' responsibilities in clinical practice continue to increase, with medical history documentation becoming an essential component. Artificial Intelligence (AI) technologies, particularly advances in Natural Language Processing (NLP), have introduced new possibilities for medical documentation. This study aims to evaluate the efficiency and quality of medical history documentation by ChatGPT-4o compared to resident physicians and explore the potential applications of AI in clinical documentation.MethodsUsing a non-inferiority design, this study compared the documentation time and quality scores between 5 resident physicians from the hematology department (with an average of 2.4 years of clinical experience) and ChatGPT-4o based on identical case materials. Medical history quality was evaluated by two attending physicians with over 10 years of clinical experience using ten case content criteria. Data were analyzed using paired t-tests and Wilcoxon signed-rank tests, with Kappa coefficients used to assess scoring consistency. Detailed scoring criteria included completeness (coverage of history elements), accuracy (correctness of information), logic (organization and coherence of content), and professionalism (appropriate use of medical terminology and format), each rated on a 10-point scale.ResultsIn terms of medical history quality, ChatGPT-4o achieved an average score of 88.9, while resident physicians scored 89.6, with no statistically significant difference between the two (p = 0.25). The Kappa coefficient between the two evaluators was 0.82, indicating good consistency in scoring. Non-inferiority testing showed that ChatGPT-4o's quality scores fell within the preset non-inferiority margin (5 points), indicating that its documentation quality was not inferior to that of resident physicians. ChatGPT-4o's average documentation time was 40.1 s, significantly shorter than the resident physicians' average of 14.9 min (p < 0.001).ConclusionWhile maintaining quality comparable to resident physicians, ChatGPT-4o significantly reduced the time required for medical history documentation. Despite these positive results, practical considerations such as data preprocessing, data security, and privacy protection must be addressed in real-world applications. Future research should further explore ChatGPT-4o's capabilities in handling complex cases and its applicability across different clinical settings. |
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| ISSN: | 2296-858X |