Email this article Machine Learning in Healthcare: Current Applications, Challenges, and Future Directions
Abstract
This paper provides an in-depth exploration of the current landscape of machine learning (ML) applications in the field of healthcare. It discusses the diverse and transformative ways in which ML is revolutionizing patient care, diagnostics, treatment planning, and drug discovery. The paper also addresses the significant challenges in implementing ML in healthcare, including data privacy, bias, and regulatory considerations.
Moreover, it offers insights into the future of ML in healthcare, envisioning a world where personalized medicine, early disease detection, and data-driven decision support become the standard. With ML playing an increasingly vital role in healthcare, this paper serves as a comprehensive resource for researchers, healthcare professionals, and policymakers aiming to harness the full potential of ML technologies to improve patient outcomes and healthcare systems.
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