Physicians' Knowledge, Perceptions and Use of Large Language Models in Clinical Practice: A cross-sectional study at Sultan Qaboos University Hospital

医生对大型语言模型的认知、理解和临床实践应用:苏丹卡布斯大学医院的一项横断面研究

阅读:1

Abstract

OBJECTIVES: This study assessed the knowledge, perceptions and use of ChatGPT and other large language models (LLMs) among physicians at Sultan Qaboos University Hospital (SQUH), Oman. It also explored perceived benefits, barriers and ethical concerns regarding artificial intelligence (AI) integration into clinical practice. METHODS: This cross-sectional study was conducted between September and December 2024 using a structured online questionnaire distributed to physicians across different specialties. The survey covered demographics, familiarity with LLMs, applications in medical education, research, clinical care, administrative tasks and ethical considerations. RESULTS: A total of 146 physicians were included (response rate = 48.7%); 65.1% were familiar with ChatGPT or other LLMs and 70.5% had used them, mainly for education (47.9%) and research (46.6%). Use in clinical practice (29.5%) and administrative tasks (18.5%) was less frequent. Most physicians perceived LLMs as enhancing research (82.9%), education (79.5%), administrative work (74.4%) and patient care (54.1%). While 89.8% believed LLMs could improve professional work, only 39.7% expressed confidence in integrating outputs while upholding academic standards. Ethical concerns were widespread (96.6%), focusing on reliability, accuracy and data privacy. Despite low awareness of ethical guidelines (15.1%), 87.0% expressed willingness to engage in AI-related training. Male physicians reported higher use for research and diagnostics (P <0.05 each), younger physicians (<30 years) more often used LLMs for education (P = 0.037) and senior physicians more frequently perceived bias in outputs (P = 0.045). CONCLUSION: Physicians at SQUH demonstrated moderate familiarity and cautious optimism towards LLMs. Addressing gaps in training and ethical awareness is crucial for responsible AI adoption in clinical and academic practice.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。