The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency - Md. Faiyazuddin; Syed Jalal Q. Rahman; Gaurav Anand; Reyaz Kausar Siddiqui; Rachana Mehta; Mahalaqua Nazli Khatib; Shilpa Gaidhane; Quazi Syed Zahiruddin; Arif Hussain; Ranjit Sah (Health Science Reports)
ABSTRACT Background and Aims Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment. Methods This review analyzes the impact of AI on healthcare using data from the Web of Science (2014–2024), focusing on keywords like AI, ML, and healthcare applications. It examines the uses and effects of AI on healthcare by synthesizing recent literature and real‐world case studies, such as Google Health and IBM Watson Health, highlighting AI technologies, their useful applications, and the difficulties in putting them into practice, including problems with data security and resource limitations. The review also discusses new developments in AI, and how they can affect society. Results The findings demonstrate how AI is enhancing the skills of medical professionals, enhancing diagnosis, and opening the door to more individualized treatment plans, as reflected in the steady rise of AI‐related healthcare publications from 158 articles (3.54%) in 2014 to 731 articles (16.33%) by 2024. Core applications like remote monitoring and predictive analytics improve operational effectiveness and patient involvement. However, there are major obstacles to the mainstream implementation of AI in healthcare, including issues with data security and budget constraints. Conclusion Healthcare may be transformed by AI, but its successful use requires ethical and responsible use. To meet the changing demands of the healthcare sector and guarantee the responsible application of AI technologies, the evaluation highlights the necessity of ongoing research, instruction, and multidisciplinary cooperation. In the future, integrating AI responsibly will be essential to optimizing its advantages and reducing related dangers.