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    AI-assisted decision-making in mild traumatic brain injury
    (BMC, 2025) Yiğit, Y; Kaynak, MF; Alkahlout, B; Ahmed, S; Günay, S; Özbek, AE
    Objective This study evaluates the potential use of ChatGPT in aiding clinical decision-making for patients with mild traumatic brain injury (TBI) by assessing the quality of responses it generates for clinical care. Methods Seventeen mild TBI case scenarios were selected from PubMed Central, and each case was analyzed by GPT-4 (March 21, 2024, version) between April 11 and April 20, 2024. Responses were evaluated by four emergency medicine specialists, who rated the ease of understanding, scientific adequacy, and satisfaction with each response using a 7-point Likert scale. Evaluators were also asked to identify critical errors, defined as mistakes in clinical care or interpretation that could lead to morbidity or mortality. The readability of GPT-4's responses was also assessed using the Flesch Reading Ease and Flesch-Kincaid Grade Level tools. Results There was no significant difference in the ease of understanding between responses with and without critical errors (p = 0.133). However, responses with critical errors significantly reduced satisfaction and scientific adequacy (p < 0.001). GPT-4 responses were significantly more difficult to read than the case descriptions (p < 0.001). Conclusion GPT-4 demonstrates potential utility in clinical decision-making for mild TBI management, offering scientifically appropriate and comprehensible responses. However, critical errors and readability issues limit its immediate implementation in emergency settings without oversight by experienced medical professionals.
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    Öğe
    Evaluating GPT-4's role in critical patient management in emergency departments
    (PUBLIC LIBRARY SCIENCE, 2025) Yiğit, Y; Günay, S; Öztürk, A; Alkahlout, B
    Introduction Recent advancements in artificial intelligence (AI) have introduced tools like ChatGPT-4, capable of interpreting visual data, including ECGs. In our study,we aimed to investigate the effectiveness of GPT-4 in interpreting ECGs and managing patient care in emergency settings. Methods Conducted from April to May 2024, this study evaluated GPT-4 using twenty case scenarios sourced from PubMed Central and the OSCE sample question book. These cases, categorized into common and rare scenarios, were analyzed by GPT-4, and its interpretations were reviewed by five experienced emergency medicine specialists. The accuracy of ECG interpretations and subsequent patient management plans were assessed using a structured evaluation framework and critical error identification. Results GPT-4 made critical errors in 46% of ECG interpretations in the OSCE group and 50% in the PubMed group. For patient management, critical errors were found in 32% of the OSCE group and 14% of the PubMed group. When ECG evaluations were included in patient management, error rates approached 50%. The inter-rater reliability among evaluators indicated good agreement (ICC = 0.725, F = 3.72, p < 0.001). Conclusion While GPT-4 shows promise in specific applications, its current limitations in accurately interpreting ECGs and managing critical patient scenarios render it inappropriate for emergency department use. Future improvements and extensive validations are essential before such AI tools can be reliably deployed in critical healthcare settings.
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    Öğe
    Evaluating large language models for renal colic imaging recommendations: a comparative analysis of Gemini, copilot, and ChatGPT-4.0
    (BMC, 2025) Yiğit, Y; Özbek, AE; Doğru, B; Günay, S; Alkahlout, B
    Background The field of natural language processing (NLP) has evolved significantly since its inception in the 1950s, with large language models (LLMs) now playing a crucial role in addressing medical challenges. Objectives This study evaluates the alignment of three prominent LLMs-Gemini, Copilot, and ChatGPT-4.0-with expert consensus on imaging recommendations for acute flank pain. Methods A total of 29 clinical vignettes representing different combinations of age, sex, pregnancy status, likelihood of stone disease, and alternative diagnoses were posed to the three LLMs (Gemini, Copilot, and ChatGPT-4.0) between March and April 2024. Responses were compared to the consensus recommendations of a multispecialty panel. The primary outcome was the rate of LLM responses matching the majority consensus. Secondary outcomes included alignment with consensus-rated perfect (9/9) or excellent (8/9) responses and agreement with any of the nine panel members. Results Gemini aligned with the majority consensus in 65.5% of cases, compared to 41.4% for both Copilot and ChatGPT-4.0. In scenarios rated as perfect or excellent by the consensus, Gemini showed 69.5% agreement, significantly higher than Copilot and ChatGPT-4.0, both at 43.4% (p = 0.045 and < 0.001, respectively). Overall, Gemini demonstrated an agreement rate of 82.7% with any of the nine reviewers, indicating superior capability in addressing complex medical inquiries. Conclusion Gemini consistently outperformed Copilot and ChatGPT-4.0 in aligning with expert consensus, suggesting its potential as a reliable tool in clinical decision-making. Further research is needed to enhance the reliability and accuracy of LLMs and to address the ethical and legal challenges associated with their integration into healthcare systems.