Abstract：
BACKGROUND: This study investigates the potential of ChatGPT-4, developed by OpenAI, in enhancing medical decision-making processes, particularly in preoperative assessments using the American Society of Anesthesiologists (ASA) scoring system. The ASA score, a critical tool in evaluating patients\' health status and anesthesia risks before surgery, categorizes patients from I to VI based on their overall health and risk factors. Despite its widespread use, determining accurate ASA scores remains a subjective process that may benefit from AI-supported assessments. This research aims to evaluate ChatGPT-4\'s capability to predict ASA scores accurately compared to expert anesthesiologists\' assessments.
METHODS: In this prospective multicentric study, ethical board approval was obtained, and the study was registered with clinicaltrials.gov (NCT06321445). We included 2851 patients from anesthesiology outpatient clinics, spanning neonates to all age groups and genders, with ASA scores between I-IV. Exclusion criteria were set for ASA V and VI scores, emergency operations, and insufficient information for ASA score determination. Data on patients\' demographics, health conditions, and ASA scores by anesthesiologists were collected and anonymized. ChatGPT-4 was then tasked with assigning ASA scores based on the standardized patient data.
RESULTS: Our results indicate a high level of concordance between ChatGPT-4 predictions and anesthesiologists\' evaluations, with Cohen\'s kappa analysis showing a kappa value of 0.858 (p = 0.000). While the model demonstrated over 90% accuracy in predicting ASA scores I to III, it showed a notable variance in ASA IV scores, suggesting a potential limitation in assessing patients with more complex health conditions.
CONCLUSIONS: The findings suggest that ChatGPT-4 can significantly contribute to the medical field by supporting anesthesiologists in preoperative assessments. This study not only demonstrates ChatGPT-4\'s efficacy in medical data analysis and decision-making but also opens new avenues for AI applications in healthcare, particularly in enhancing patient safety and optimizing surgical outcomes. Further research is needed to refine AI models for complex case assessments and integrate them seamlessly into clinical workflows.
METHODS: In this prospective multicentric study, ethical board approval was obtained, and the study was registered with clinicaltrials.gov (NCT06321445). We included 2851 patients from anesthesiology outpatient clinics, spanning neonates to all age groups and genders, with ASA scores between I-IV. Exclusion criteria were set for ASA V and VI scores, emergency operations, and insufficient information for ASA score determination. Data on patients\' demographics, health conditions, and ASA scores by anesthesiologists were collected and anonymized. ChatGPT-4 was then tasked with assigning ASA scores based on the standardized patient data.
RESULTS: Our results indicate a high level of concordance between ChatGPT-4 predictions and anesthesiologists\' evaluations, with Cohen\'s kappa analysis showing a kappa value of 0.858 (p = 0.000). While the model demonstrated over 90% accuracy in predicting ASA scores I to III, it showed a notable variance in ASA IV scores, suggesting a potential limitation in assessing patients with more complex health conditions.
CONCLUSIONS: The findings suggest that ChatGPT-4 can significantly contribute to the medical field by supporting anesthesiologists in preoperative assessments. This study not only demonstrates ChatGPT-4\'s efficacy in medical data analysis and decision-making but also opens new avenues for AI applications in healthcare, particularly in enhancing patient safety and optimizing surgical outcomes. Further research is needed to refine AI models for complex case assessments and integrate them seamlessly into clinical workflows.
摘要：
背景：这项研究调查了由OpenAI开发的ChatGPT-4的潜力，在加强医疗决策过程中，特别是在使用美国麻醉医师协会（ASA）评分系统的术前评估中。ASA的分数,手术前评估患者健康状况和麻醉风险的重要工具，根据患者的整体健康状况和危险因素,将患者从I至VI进行分类.尽管它广泛使用，确定准确的ASA分数仍然是一个主观过程，可能会从AI支持的评估中受益。这项研究旨在评估ChatGPT-4预测ASA评分的能力，与麻醉专家的评估相比。
方法：在这项前瞻性多中心研究中，获得伦理委员会批准，该研究已在clinicaltrials.gov（NCT06321445）中注册。我们纳入了麻醉科门诊的2851名患者，涵盖所有年龄组和性别的新生儿，ASA得分在I-IV之间。排除标准为ASAV和VI评分设置，紧急行动,和ASA分数确定的信息不足。关于患者人口统计学的数据，健康状况,麻醉医师的ASA评分被收集并匿名化.然后，ChatGPT-4的任务是根据标准化的患者数据分配ASA评分。
结果：我们的结果表明ChatGPT-4预测与麻醉师评估之间的高度一致性，Cohen的kappa分析显示kappa值为0.858(p=0.000)。虽然该模型在预测ASA分数I至III方面表现出超过90%的准确性，它显示了ASAIV分数的显着差异，提示在评估更复杂的健康状况患者方面存在潜在的局限性。
结论：研究结果表明，ChatGPT-4通过支持麻醉医师进行术前评估，可以为医学领域做出显著贡献。这项研究不仅证明了ChatGPT-4在医疗数据分析和决策方面的有效性，而且为AI在医疗保健中的应用开辟了新的途径。特别是在提高患者安全性和优化手术结果方面。需要进一步的研究来完善复杂病例评估的AI模型，并将其无缝集成到临床工作流程中。
方法：在这项前瞻性多中心研究中，获得伦理委员会批准，该研究已在clinicaltrials.gov（NCT06321445）中注册。我们纳入了麻醉科门诊的2851名患者，涵盖所有年龄组和性别的新生儿，ASA得分在I-IV之间。排除标准为ASAV和VI评分设置，紧急行动,和ASA分数确定的信息不足。关于患者人口统计学的数据，健康状况,麻醉医师的ASA评分被收集并匿名化.然后，ChatGPT-4的任务是根据标准化的患者数据分配ASA评分。
结果：我们的结果表明ChatGPT-4预测与麻醉师评估之间的高度一致性，Cohen的kappa分析显示kappa值为0.858(p=0.000)。虽然该模型在预测ASA分数I至III方面表现出超过90%的准确性，它显示了ASAIV分数的显着差异，提示在评估更复杂的健康状况患者方面存在潜在的局限性。
结论：研究结果表明，ChatGPT-4通过支持麻醉医师进行术前评估，可以为医学领域做出显著贡献。这项研究不仅证明了ChatGPT-4在医疗数据分析和决策方面的有效性，而且为AI在医疗保健中的应用开辟了新的途径。特别是在提高患者安全性和优化手术结果方面。需要进一步的研究来完善复杂病例评估的AI模型，并将其无缝集成到临床工作流程中。
