Machine learning and medical education

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Artificial intelligence (AI) is designed to offer distinct medicine and health. The clinical and biomedical research communities are continuously tapping on this technology to create tools for diagnosis and prediction as well as to enhance delivery and capability of healthcare. New findings are being fostered in an unique manner and the developed ones have received regulatory approval and are integrated into routine medical practice. However, the medical school curriculum, including the graduate medical education and other teaching programs within academic hospitals across the United states and beyond have yet to come to terms with teaching students and trainees on this rising trend. Some expert opinions have shed light on the benefits and limitations in relation to the use of ML in medicine. However, the aspect related to formally nurturing the present and future medical professionals have yet to be discussed in depth.

The growing approval of machine learning (ML) techniques for medical procedures is evident from the increasing amount of research carried out on this topic, the number of products that are receiving regulatory approvals along with the entrepreneurial efforts in this space over the past few years. In addition, there has been an observed large percentile of venture capital (VC) backed AI startups in the healthcare sector, and these numbers are increasing steadily. VC funding for healthcare AI organisations was approximately $3.6 billion in the last 5 years. The above facts highlighted the increasing recognition of the value that ML can possibly contribute to the medical landscape.

Despite so, there is a lack of direct access to relevant ML education for clinicians and biomedical researchers. Various factors attribute to the failure of ML to be integrated within undergraduate and graduate medical education training. At present, there is no accreditation requirements related to retain curricular hours in the present schema with the emerging biomedical knowledge and demands for new content segments. In the United States, assessment in undergraduate medical education, places great emphasis on the preparation of licensing exams and a recent competency focus on entrustable professional activities (EPA’s), none of which involves AI. In addition, medical schools fall short of faculty expertise needed to teach this content which is mainly conducted in computer science, mathematics and engineering faculties. The lack of mentorship and faculty role modeling are compelling issues for students to shift from the preclinical to clinical environment and attempting to establish an understanding of how AI knowledge can be administered and utilised in the clinical setting. AI influences patients and patient care. Hence, ML and its applications should be taught within medical school and needs to be streamlined to train future clinicians and biomedical scientists to overcome data-driven challenges that can directly affect patient care in the near future.

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