Artificial intelligence (AI) and deep learning have quickly developed for image-based diagnostics, disease prediction and clinical decision support. Medical informatics/education in medicine have been huge changes caused by these technologies. To analyze such a complex biomedical data, deep learning architectures are employed. This narrative review discusses how deep learning has transformed medical education and informatics, as it is used in medical imaging, genomic analysis, predictive analytics, and smart monitoring systems. In medical education, deep learning is employed for interactive simulations, automated grading, and individual learning; in informatics, for diagnosis accuracy, decision-making, as well as patient care. The future directions show that explainable AI (XAI), federated learning and ethical frameworks are required to be integrated for enhancing transparency and reliability. Such changes are leading us into a revolution in healthcare and medical education with deep learning, where the efficiency, personalization and evidence-based are improved.
Güldoğan et al. (Tue,) studied this question.
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