Deep learning is transforming biomedical imaging and holds great promise for enhancing diagnostics and treatment planning for a wide range of medical specialties. In this systematic review, the PRISMA guidelines are used to highlight the recent deep-learning applications in medical diagnostics for diabetic retinopathy, breast cancer, skin lesions, lung nodules, and stroke via medical image analysis. We review techniques taken by some pioneering research groups, including Google’s DeepMind, NYU, and Stanford University, that have used convolutional neural networks and deep learning architectures to obtain diagnostic accuracies comparable to or even superior to human specialists. An extensive literature search was conducted in PubMed, PMC, and Google Scholar between January 2014 and April 2024 using keywords including ‘deep learning’ and ‘biomedical imaging’. The review attempts to highlight how these models speed up the diagnostic process, enhance the accuracy of medical assessments, and potentially remove unnecessary interventions. Data requirement problems, issues of computational demand, and issues of bounding model generalizability are still concerns. The path ahead involves the utilization of more powerful systems and the determination of more generalizable models to be implemented in a clinical setting for the predictive analytics of Alzheimer’s disease. This review paper underscores the transformative potential of deep learning in healthcare to critically outline where it stands today and to lay down lines along which future innovation and implementation can proceed.