Transformative Impacts of Artificial Intelligence on Healthcare

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Authors

Chandan Shivamallu (ed)
Department of Biotechnology and Bioinformatics, JSS Academy of Higher Education and Research, Mysuru, Karnataka, India-570015
Shiva Prasad Kollur (ed)
School of Physical Sciences, Amrita Vishwa Vidyapeetham, Mysuru - 570006
Chandrashekar Srinivasa (ed)
Department of Studies in Biotechnology, Davangere University, Davangere, Karnataka
Sharanagouda S Patil (ed)
ICAR–National Institute of Veterinary Epidemiology and Disease Informatics

Keywords:

Artificial Intelligence, Machine Learning, Big Data Analytics, Digital Health, Precision Medicine, Telemedicine, Robotics in Healthcare

Synopsis

It is a true revolution in medicine with the advent of artificial intelligence (AI) and healthcare. Many scholars define AI as "an extremely powerful and disrupting area of computer science with the possibility of radically changing the practice of medicine and the delivery of healthcare." Only a few years ago, AI was nothing but conceptual ideas. Today, AI is evolving rapidly, from theoretical concepts to practical applications which include diagnostics, treatment, and administration of healthcare in clinics and hospitals. With AI-based systems beginning to diagnose patients at faster rates and provide precision diagnosis, and begin to develop personalized treatments based on the needs of each patient, Microsoft CEO Satya Nadella believes AI may be "the most transformational technology of our time," and that "healthcare is perhaps AI's most urgent application." These changes signify a radical change in patient care: from data driven predictive models and clinical decision support, to automated image analysis and remote patient monitoring. In developing this collection of papers, we acknowledge that AI's effects are no longer speculative, but are currently occurring in laboratories, clinics, and health systems around the world.

AI is inherently an interdisciplinary approach to transforming healthcare. State-of-the-art algorithms and systems are developed in collaboration among computer scientists, biomedical researchers, clinicians, and engineers. Reviews also demonstrate the importance of "interdisciplinary dialogue between researchers, clinicians, and technologists" to guide AI implementations in medicine. This volume exemplifies that collaborative spirit: it presents experts from a variety of areas including machine learning, medical informatics, epidemiology, and health management. The chapters range from foundational AI concepts and frameworks to special applications, including topics such as predictive analytics in patient care, intelligent hospital operations, and even robots in surgery. By presenting a collective view, we intend to show how the same AI techniques can be used to support both the research bench and the bedside. For researchers, the volume represents methodologies that are rigorous and emerging evidence; for clinicians, it represents practical tools and possible workflows; for students, it represents a comprehensive map of an evolving discipline. Furthermore, we emphasized academic rigor and evidence-based analysis so that readers from all three of these disciplines could use and build upon the material.

The reason for preparing this volume is the unprecedented speed and magnitude of AI in healthcare. Never before has the rate of technological development been so fast or the consequences so great. In 2023 alone, the FDA approved 220 AI enabled medical devices; in 2015 there were only six demonstrating the explosive growth in practical applications. At the same time, global health challenges (aging populations, chronic diseases, pandemics) have increased the urgency for new solutions. AI presents a tool set to aid clinician workloads, personalize medicine, and alleviate disparities in access. Given this context, we have prepared papers that report on the latest developments in AI for healthcare and critically evaluate those developments. Rather than merely extolling AI's potential, the authors evaluate how AI systems function in clinical trials, how they are integrated into the workflow of healthcare providers, and how they compare to established standards of care. Our goal is to present the state of the art in AI for healthcare using academic transparency and thoroughness, and provide a basis for future research and practice.

We believe that the themes represented in this volume will continue to influence the course of medicine in the coming decades. The chapters in this volume illustrate how AI enhances diagnostic accuracy, tailors treatments, and streamlines healthcare operations in ways that were unimaginable just a few generations ago. The volume does not avoid difficult issues: several authors describe ethical frameworks, data privacy, and the need for equal access to AI. By describing both the opportunities and the challenges of realizing AI's full potential, this volume illustrates that effective use of AI in medicine depends on careful stewardship and cooperative effort across multiple disciplines. We expect that readers of this volume will understand how AI can be used to improve patient outcomes and healthcare systems.

Overall, we believe that Transformative Impacts of Artificial Intelligence on Healthcare is a timely and scholarly collection that emphasizes both significance and rigor. We believe that by integrating current knowledge, from theory to application, and from the laboratory to the clinic, we provide a reference that will serve as a source of inspiration for research, a resource for guiding clinical innovation, and a textbook for educating the next generation of practitioners. We believe that this work will inspire its readers to engage critically and positively with the frontiers of healthcare technology, and thereby improve medicine for patients worldwide.

Chapters

References

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Published

3 November 2025
Copyright (c) 2025 Vijay Ramesh, Bhargav Shreevatsa; Chandan Shivamallu, Chandrashekar Srinivasa, Sharanagouda S Patil, Shiva Prasad Kollur; Prajwal N Siddalingaswamy, Kavitha Raj V, Lavanya D Kumar, Supreet S. Naik, Pruthvi Raj S, Yashwanth D, Abhay Mahesh, Bolla Bhavani Madhu Meghana, Nanjunda Swamy Shivananju, Prajwal Bhanu, Pradeep M, Meghashree Gangatikara, Siddesh V Siddalingegowda, Varshini Narayan, Anisha Jain, Chandan Dharmashekara, Ashwini P, Rajendra Prasad M.L, Sathyamurthy Srinivas

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978-93-7185-152-7

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ISBN-13 (15)

978-93-7185-796-3

How to Cite

Ramesh, V. ., & Shreevatsa, B. . (2025). Transformative Impacts of Artificial Intelligence on Healthcare (C. . Shivamallu, S. P. . Kollur, C. . Srinivasa, & S. . S Patil , Eds.). Deep Science Publishing. https://doi.org/10.70593/978-93-7185-152-7
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