Recent Advances, Challenges, and Applications of Deep Learning in Healthcare Systems for Medical Diagnosis and Treatment

Abstract

In the realm of healthcare, the integration of Deep Learning (DL) stands as a potent force, propelling advancements in medical diagnosis and treatment. This paper navigates recent strides, persistent hurdles, and emerging applications within this synergy. DL basics are elucidated initially, demonstrating neural networks' role in data analysis. Progressing further, we unveil DL's robust applications in medical diagnosis, particularly via Convolutional Neural Networks, revolutionizing image-based disease detection. Yet, this frontier is not devoid of challenges, encompassing data privacy concerns and model interpretability. In tandem, DL empowers personalized treatment by predicting disease trajectories and expediting drug discovery. Expanding horizons, DL streamlines healthcare management through resource optimization and digitized records. In conclusion, this synthesis of DL and healthcare signifies a transformative trajectory, promising refined diagnostics and patient-centric treatments, though not devoid of ethical and technical intricacies.

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