Kayacan Erdoğan Esra, Babaoğlu Hakan
Department of Rheumatology, Division of Internal Medicine, Ankara Bilkent City Hospital, Ankara 06800, Turkey.
J Clin Med. 2024 Dec 5;13(23):7405. doi: 10.3390/jcm13237405.
The integration of artificial intelligence (AI) in medicine has progressed from rule-based systems to advanced models and is showing potential in clinical decision-making. In this study, the psychological impact of AI collaboration in clinical practice is assessed, highlighting its role as a support tool for medical residents. This study aimed to compare clinical decision-making approaches of junior rheumatology residents with both trained and untrained AI models in clinical reasoning, pre-diagnosis, first-line, and second-line management stages. Ten junior rheumatology residents and two GPT-4 models (trained and untrained) responded to 10 clinical cases, encompassing diagnostic and treatment challenges in inflammatory arthritis. The cases were evaluated using the Revised-IDEA (R-IDEA) scoring system and additional case management metrics. In addition to scoring clinical case performance, residents' attitudes toward AI integration in clinical practice were assessed through a structured questionnaire, focusing on perceptions of AI's potential after reviewing the trained GPT-4's answers. Trained GPT-4 outperformed residents across all stages, achieving significantly higher median R-IDEA scores and superior performance in pre-diagnosis, first-line, and second-line management phases. Residents expressed a positive attitude toward AI integration, with 60% favoring AI as a supportive tool in clinical practice, anticipating benefits in competence, fatigue, and burnout. Trained GPT-4 models outperform junior residents in clinical reasoning and management of rheumatology cases. Residents' positive attitudes toward AI suggest its potential as a supportive tool to enhance confidence and reduce uncertainty in clinical practice. Trained GPT-4 may be used as a supplementary tool during the early years of residency.
人工智能（AI）在医学领域的整合已从基于规则的系统发展到先进模型，并在临床决策中显示出潜力。在本研究中，评估了AI协作在临床实践中的心理影响，突出了其作为医学住院医师支持工具的作用。本研究旨在比较初级风湿病住院医师在临床推理、预诊断、一线和二线管理阶段与经过训练和未经训练的AI模型的临床决策方法。十名初级风湿病住院医师和两个GPT-4模型（经过训练和未经训练）对10个临床病例做出了回应，这些病例涵盖了炎症性关节炎的诊断和治疗挑战。使用修订后的IDEA（R-IDEA）评分系统和其他病例管理指标对病例进行评估。除了对临床病例表现进行评分外，还通过一份结构化问卷评估了住院医师对AI整合到临床实践中的态度，重点是在查看经过训练的GPT-4的答案后对AI潜力的看法。经过训练的GPT-4在所有阶段的表现均优于住院医师，在预诊断、一线和二线管理阶段的R-IDEA中位数得分显著更高，表现更优。住院医师对AI整合表达了积极态度，60%的人赞成在临床实践中使用AI作为支持工具，预计在能力、疲劳和职业倦怠方面会有好处。经过训练的GPT-4模型在风湿病病例的临床推理和管理方面优于初级住院医师。住院医师对AI的积极态度表明其有潜力作为一种支持工具，增强临床实践中的信心并减少不确定性。经过训练的GPT-4可在住院医师培训的早期用作辅助工具。
