AI in Healthcare: Its Impact on Diagnostic and Therapeutic Perspectives

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in the healthcare sector, significantly enhancing diagnostic accuracy and therapeutic effectiveness. By leveraging advanced computational techniques such as machine learning, deep learning, and natural language processing, AI systems can analyze complex medical data and support clinical decision-making. This paper examines the impact of AI in healthcare from both diagnostic and therapeutic perspectives. It reviews existing literature to highlight AI-driven innovations in medical imaging, disease prediction, personalized treatment planning, and robotic-assisted therapies. A qualitative methodology based on secondary data analysis is adopted, drawing on peer-reviewed journals, clinical studies, and authoritative reports. Data analysis reveals that AI improves early disease detection, optimizes treatment outcomes, and enhances healthcare efficiency while also presenting challenges related to data privacy, ethical concerns, and regulatory compliance. The discussion explores the implications of AI adoption for healthcare professionals, patients, and healthcare systems. The paper concludes that AI has the potential to revolutionize healthcare delivery by enabling more accurate diagnostics and personalized therapies, provided that ethical and governance issues are effectively addressed.

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