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Abstract：
Background This study aimed to evaluate the efficacy of ChatGPT, an advanced natural language processing model, in adapting and synthesizing clinical guidelines for diabetic ketoacidosis (DKA) by comparing and contrasting different guideline sources. Methodology We employed a comprehensive comparison approach and examined three reputable guideline sources: Diabetes Canada Clinical Practice Guidelines Expert Committee (2018), Emergency Management of Hyperglycaemia in Primary Care, and Joint British Diabetes Societies (JBDS) 02 The Management of Diabetic Ketoacidosis in Adults. Data extraction focused on diagnostic criteria, risk factors, signs and symptoms, investigations, and treatment recommendations. We compared the synthesized guidelines generated by ChatGPT and identified any misreporting or non-reporting errors. Results ChatGPT was capable of generating a comprehensive table comparing the guidelines. However, multiple recurrent errors, including misreporting and non-reporting errors, were identified, rendering the results unreliable. Additionally, inconsistencies were observed in the repeated reporting of data. The study highlights the limitations of using ChatGPT for the adaptation of clinical guidelines without expert human intervention. Conclusions Although ChatGPT demonstrates the potential for the synthesis of clinical guidelines, the presence of multiple recurrent errors and inconsistencies underscores the need for expert human intervention and validation. Future research should focus on improving the accuracy and reliability of ChatGPT, as well as exploring its potential applications in other areas of clinical practice and guideline development.
摘要：
背景本研究旨在评估ChatGPT的疗效，先进的自然语言处理模型,通过比较和对比不同的指南来源来适应和综合糖尿病酮症酸中毒（DKA）的临床指南。方法我们采用了全面的比较方法，并检查了三个著名的指南来源：加拿大糖尿病临床实践指南专家委员会（2018），初级保健中高血糖的应急管理，联合英国糖尿病协会（JBDS）02成人糖尿病酮症酸中毒的管理。数据提取侧重于诊断标准，危险因素，症状和体征,调查,和治疗建议。我们比较了ChatGPT生成的综合指南，并确定了任何误报或未报告的错误。结果ChatGPT能够生成比较指南的综合表格。然而,多个反复出现的错误，包括误报和未报告错误，被确认,使结果不可靠。此外,在重复报告数据中观察到不一致.该研究强调了使用ChatGPT在没有专家人工干预的情况下适应临床指南的局限性。结论虽然ChatGPT证明了临床指南合成的潜力，多次反复出现的错误和不一致现象的存在凸显了专家人工干预和验证的必要性.未来的研究应该集中在提高ChatGPT的准确性和可靠性上，以及探索其在临床实践和指南开发其他领域的潜在应用。
