Ong Qi Chwen, Ang Chin-Siang, Lai Nai Ming, Atun Rifat, Car Josip
School of Life Course and Population Sciences, King's College London, London, United Kingdom.
School of Public Health, Imperial College London, White City Campus, 90 Wood Lane, London, W12 0BZ, United Kingdom, 44 20-7589-511.
J Med Internet Res. 2025 May 9;27:e72186. doi: 10.2196/72186.
In this secondary analysis of a multinational Delphi study, experts from low- and middle-income countries were less likely than those from high-income countries to consider artificial intelligence (AI) learning outcomes mandatory in preregistration medical education, potentially reflecting underlying global inequalities in medical AI education and highlighting the need for adaptable AI competency frameworks.
在这项对一项跨国德尔菲研究的二次分析中，与高收入国家的专家相比，低收入和中等收入国家的专家在预注册医学教育中不太可能将人工智能（AI）学习成果视为强制性要求，这可能反映了医学人工智能教育中潜在的全球不平等，并凸显了制定适应性强的人工智能能力框架的必要性。
