Study of Deep Learning in Medical Education: Opportunities, Achievements and Future Challenges.

Introduction: In this era of progress, interest has developed regarding advancing deep learning (DL) in medicine. However, there has been reluctance to use deep learning, particularly among medical educators. The limitations of previous research were examined in this study, along with the extent to which DL can be used in medical education and its potential impact on educational quality. We were interested in discussing DL's prospects, and determining whether we could benefit from it in medical education. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was used to manage this review procedure. Six databases were searched carefully to obtain relevant studies. Our search identified 981 articles from the database based on our standards. After filtering the duplicated articles, 11 studies were included in the systematic review. Results: The results showed that DL applications attracted researchers' attention in the medical and education technology owing to their effectiveness to provide the personalized assistance and feedback. Furthermore, the majority of research concentrated on teaching medical students how to utilize DL applications in the classroom, and all of them tried to improve medical students' proficiency with DL instruments in practical applications. Deep learning components in medical learning environments have two segments-in the educational settings like speech recognition or Video content analysis for affecting students' learning, and in the medical settings, applying deep learning from diagnosis to prevention. An integration of them can work better in medical education. Conclusion: Medical education uses DL to improve the students' education. DL is a powerful instrument which has become more famous in terms of superb outcomes. Besides, using DL in medical education is likely to continue as a hotly debated area of research and a well-known classroom strategy.