Deep Learning Approaches in Healthcare: A Comprehensive Review of Techniques and Applications

Abstract

Introduction: Deep learning is a method used in artificial intelligence that processes data in a way that is similar to how the human brain does it while also developing decision-making patterns. Deep learning has revolutionized the use of artificial intelligence, and has been deployed in different fields of healthcare applications such as computer vision, natural language processing, and signal processing. Deeply used in various tasks of healthcare.

Objectives: In this paper, we present a comprehensive review and analysis of deep learning techniques used in healthcare applications. This paper explores the diverse range of healthcare domains where deep learning has shown promising results, including medical imaging, electronic health records (EHR), genomics, drug discovery, and disease prediction.

Methods: The paper has discussed the main challenges and opportunities for applying deep learning models in healthcare, such as temporal modeling, interpretability and generalizability. In addition, we highlight recent advances in deep learning architectures, including convolutional neural networks, recurrent neural networks, and generative adversarial networks, and their specific applications in healthcare. Finally, we provide insights on future directions and potential research avenues for advancing deep learning techniques in medicine.

Results: Techniques such as CNNs, RNNs, LSTMs, GANs, autoencoders, and hybrid models have been successfully applied to tasks like medical imaging, EHR analysis, genomics, disease prediction, and drug discovery. The reviewed literature shows that deep learning models often outperform traditional machine learning methods in both accuracy and adaptability. Notable results include high prediction accuracy in diseases such as Alzheimer’s, Parkinson’s, diabetes, skin cancer, and cardiovascular risks. Despite these advancements, challenges remain in interpretability, generalization, and data availability.

Conclusions: Deep learning has evolved as a transformative technology in healthcare, enabling great advancements in a variety of areas, including medical imaging, honor analysis, genomics, drug discovery, and disease prediction. The ability to model complex patterns across large and diverse data records opened up new opportunities to improve diagnosis, treatment planning, and patient outcomes. Despite its incredible advancements, challenges such as model interpretation, data protection, and model generalization remain active. Latest innovations in deep learning architectures such as CNNS, RNNS, and Goose continue to surpass the limits of what is possible in medical applications. Considering the future, interdisciplinary collaboration and responsible AI practices will be critical to implementing deep learning breakthroughs in reliable, true health solutions.