Artificial Intelligence for Healthcare and Medical Education: A Systematic Review

American Journal of Translational Research (07/15/23) Vol. 15, No. 7, P. 4820 Sun, Li; Yin, Changhao; Xu, Qiuling; et al.

A systematic review analyzed the current status and challenges of artificial intelligence (AI) in healthcare and medical education. The researchers reviewed English databases for appropriate articles, yielding 5,720 papers that were ultimately narrowed down to 25. Analysis indicated that AI technology is primarily employed in undergraduate/postgraduate medical education, clinical professional training and continuing medical education. The bulk of the articles detailed AI's use in clinical specialty training and continuing education, but a small number described the use of the technology to flip the classroom and enhance learners' hands-on skills via virtual reality. However, medical students or residents familiar with AI, mobile health apps and telemedicine are still relatively few in number. The researchers found that current challenges for using AI in healthcare education include difficulties in the field of radiology, a dearth of high-fidelity simulation training and technology still in early developmental stages. The unavailability of many high-quality clinical imaging datasets for training and validation and the need for consent to use AI in medical education are additional obstacles. The findings also highlighted a need for accreditation standards and licenses to bring AI into medical education, little evidence on AI's impact on healthcare and medical education and a lack of core AI competitiveness and AI faculty. "In the future, we need to pay close attention to the problems and challenges that AI poses to medicine and medical education, and work with the scholars and society to drive medical education toward higher quality, efficiency and sustainability," the researchers concluded.

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