Paper Contents
Abstract
Artificial Intelligence (AI) has become an essential tool in healthcare, driving revolutionary advancements in both research and clinical practice. Its integration has transformed diagnostics, treatment, patient care, and medical research. Technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) enable the rapid and accurate analysis of vast datasets, from electronic health records (EHRs) to genomic information.In drug discovery, AI accelerates therapeutic identification through molecular simulation and clinical trial analysis. Personalized medicine benefits significantly, with AI predicting treatment responses based on individual genetic and environmental profiles. AI also enhances medical imaging accuracy, often surpassing human experts in tasks like detecting anomalies in radiographs or MRIs. Additionally, predictive analytics improve hospital operations and patient flow by forecasting clinical deterioration and optimizing resource allocation.Nevertheless, AI adoption in healthcare faces challenges including data privacy, ethical concerns, and algorithmic bias. Overcoming these barriers is vital for equitable and widespread implementation.Top of FormBottom of FormKeywords: Artificial Intelligence (AI) , Machine Learning (ML), Personalized Medicine, Medical Imaging, Clinical Decision Support
Copyright
Copyright © 2025 Pallavi Rajendra Thigale . This is an open access article distributed under the Creative Commons Attribution License.
