Harnessing Machine Learning and Deep Learning in Healthcare From Early Diagnosis to Personalized Treatment

Abstract

Machine learning (ML) and deep learning (DL) are transforming healthcare by improving patient outcomes, reducing costs, and accelerating drug development. ML algorithms analyze large datasets such as EHRs, medical imaging, and genomics to enable early disease detection and personalized treatments. The current work highlights new approaches in pharmaceutical design and predicts medication side effects. Deep Learning (DL), a branch of AI using neural networks, excels in medical imaging, identifying subtle patterns in MRIs and X-rays. The current manuscript highlights how DL models can identify genetic markers linked to diseases like cancer, Parkinson's, and Alzheimer's. Integrating ML and DL into clinical workflows empowers healthcare professionals with data-driven tools for better decision-making. However, some challenges remain, including ensuring data privacy, and security, addressing biases in algorithms. Collaboration between healthcare providers, researchers, and tech firms is essential for the ethical and effective adoption of these technologies have been discussed in the work.

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