 Corresponding author: Dinesh Deckker. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. From stethoscopes to supercomputers: The AI revolution in medicine: A review Dinesh Deckker 1, * , and Subhashini Sumanasekara 2 1 Department of Technology, Wrexham University, United Kingdom. 2 Department of Technology, University of Gloucestershire, United Kingdom. World Journal of Advanced Research and Reviews, 2025, 26(01), 1114-1131 Publication history: Received on 24 February 2025; revised on 07 April 2025; accepted on 09 April 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.1.1138 Abstract Artificial Intelligence (AI) has rapidly emerged as a transformative force in modern medicine, revolutionising diagnostics, treatment personalisation, and clinical decision-making. This review synthesises current literature on AI's evolution, applications, challenges, and future directions in healthcare. From early rule-based systems to advanced deep learning algorithms, AI has consistently demonstrated capabilities that rival and enhance human expertise— particularly in imaging, predictive analytics, and drug discovery. The role of AI in global health is also expanding, offering scalable solutions to reduce disparities in low-resource settings. However, the integration of AI raises ethical and legal concerns, including data privacy, algorithmic bias, and unclear accountability frameworks. Drawing on the Technology Acceptance Model (TAM), Diffusion of Innovations Theory, and Principlism, this review highlights theoretical perspectives essential to understanding AI adoption and governance. The paper concludes with a call for longitudinal studies, ethical frameworks, and policy innovations to support AI's responsible and equitable deployment in the medical field. Keywords: Artificial Intelligence; Medical Diagnostics; Personalized Medicine; Clinical Decision Support; Medical Imaging; Predictive Analytics; Healthcare Ethics; Algorithmic Bias; Global Health; Explainable AI 1. Introduction Medical professionals are transitioning into a new era of healthcare delivery, as Artificial Intelligence combines computational capabilities with clinical practice. Artificial Intelligence has progressed beyond the speculative stage to offer direct and transformative benefits for disease diagnosis, treatment design, and patient management [1]. Using AI- powered systems demonstrates both technological advancement and a significant transformation of medical practice methods, though it replaces traditional diagnosis tools with digital systems. The combination of machine learning, natural language processing, and computer vision technology enables artificial intelligence to analyse extensive medical datasets with superior accuracy and great speed. The data analysed through these capabilities proves especially helpful in radiology, dermatology, and genomic fields, where precision and pattern recognition are crucial [2, 3]. AI algorithms achieve comparable labeling outcomes to expert clinicians when performing check-ups through image-based analysis and sometimes provide even better results (Dodda et al., 2024). AI has established itself as an essential component that helps generate personalised medical predictions. AI examines genomic, proteomic, and patient historical data to predict disease risks and create patient-specific medical treatments, representing progress in precision healthcare [4, 5]. These abilities represent a significant shift from traditional treatment protocols, evolving toward more complex, data-driven assessment procedures.