Transforming Healthcare: Deep Learning Techniques and Their Applications

Abstract

One of the most prominent sectors high on the benefits of the deep learning (DL) revolution, a category of artificial intelligence (AI), is healthcare. DL models use multilayered artificial neural networks (ANNs) to learn from vast amounts of data, detect complex patterns, and make forecasts without manual feature extraction. DL techniques have shown great potential for optimization of clinical workflows, individualization of therapies, automation of complex tasks such as robotic surgery, medical image analysis, patient monitoring, and increase the accurate diagnosis [3]. This study provides an overview of the basic deep learning techniques used, such as reinforcement learning, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), and their applications in the medical field. Unsupervised learning algorithms all possess the great potential for dramatically increasing data efficiency and reducing the demand for large, labeled datasets are also analyzed for the future of deep learning in this paper. DL has the potential to reshape healthcare, support medical diagnosis, and improve therapies. This potential is only likely to grow as both the availability of data and computational capabilities expand, providing new opportunities for better patient outcomes and more efficient healthcare systems.

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