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For example, Katz et al [28] reported that GPT-4’s accuracy rates ranged from 17.42% (n=21) to 74.7% (n=90) across various medical disciplines. In contrast, our study found GPT-4’s accuracy rates ranged more narrowly, from 53.5% (n=704) to 60.35% (n=1076). This discrepancy could be partially attributed to the differing medical disciplines emphasized in each study, as well as variations in question structure.
J Med Internet Res 2025;27:e64452
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This is also reflected in cluster 1’s increased message volume and messaging behavior, most likely highlighting their role in clinical decision-making. However, such a centralized communication structure may also increase physicians’ workload and cognitive burden arising from an increased messaging volume [57].
Similarly, there was one cluster (cluster 4) of nurses and medical assistants who had fewer connections and were not as central within the network.
JMIR Med Inform 2025;13:e66544
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We aim to expand on the previous literature by assessing the readability of heart failure–related online PEMs from renowned cardiology institutions, assessing GPT-4’s ability to improve the readability of these PEMs, and comparing the accuracy and comprehensiveness between institutional PEMs and GPT-4’s revised PEMs.
JMIR Cardio 2025;9:e68817
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In our study, we implemented this validation approach by comparing GPT-4’s classifications of synthetic posts against those from other leading LLM platforms.
J Med Internet Res 2025;27:e65226
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