Machine Learning in Healthcare: Revolutionizing Disease Diagnosis and Treatment
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Abstract
Machine learning (ML) has emerged as a transformative technology in healthcare, revolutionizing disease diagnosis and treatment paradigms. This research explores the profound impact of ML algorithms in augmenting healthcare systems by enhancing disease identification, prediction, and personalized treatment strategies. The paper reviews the diverse applications of ML techniques, ranging from predictive analytics for early disease detection to precision medicine tailored to individual patient profiles. It examines the integration of ML algorithms into medical imaging analysis, electronic health records (EHRs), genomics, and drug discovery processes, underscoring their pivotal role in improving diagnostic accuracy and therapeutic outcomes. The review discusses the challenges and opportunities associated with the widespread adoption of ML in healthcare, addressing concerns related to data privacy, algorithm transparency, and ethical considerations. Additionally, case studies illustrating successful ML implementations in healthcare settings provide insights into the practical benefits and limitations of these technologies.
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References
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