Discover how brain-inspired, neuromorphic AI is shattering the limits of classical computing to revolutionize medicine by delivering the real-time, adaptive intelligence needed to transform everything from robotic surgeries and precision diagnostics to the future of patient care.

Current AI technologies often rely on classical computational models, which may struggle to replicate the complex, adaptive learning of the brain. Neuromorphic computing overcomes these limitations, processing medical data faster and more intelligently, which is particularly valuable in high-stakes environments like healthcare. Brain-inspired algorithms can significantly improve disease diagnosis, predictive analytics, robotic surgeries, patient monitoring, and neuroprosthetics by adapting in real time, like the human brain itself. This book focuses on the integration of neuromorphic computing into healthcare. Neuromorphic computing, which uses brain-like algorithms and architectures, has the potential to revolutionize healthcare by enhancing real-time diagnostics, improving patient outcomes, enabling personalized medicine, and transforming healthcare systems into more adaptive and efficient entities. The book explores the potential applications of neuromorphic computing in healthcare, examining topics like brain-computer interfaces, AI-enhanced diagnostics, decision support systems, personalized medicine, biomedical engineering, and the ethical issues that arise with the deployment of AI in medicine. This comprehensive exploration provides a detailed understanding of how brain-inspired AI and neuromorphic technologies are set to transform healthcare. It serves as a guide for researchers, healthcare professionals, and policymakers with a focus on technical advancements, practical implementations, and future trends in neuromorphic computing applied to the healthcare field.

Readers will find the volume:

• Introduces the fundamental principles of neuromorphic computing and its contrast with traditional AI;
• Explores real-world applications in disease diagnosis, robotic surgeries, clinical decision-making, and personalized healthcare;
• Discusses technical challenges like data scalability, hardware efficiency, and integrating systems into existing healthcare workflows;
• Addresses ethical and legal considerations in deploying neuromorphic AI in healthcare, including patient data privacy and fairness in decision-making;
• Looks to the future of healthcare innovation, driven by neuromorphic computing, with applications in neuroprosthetics and health monitoring systems.

Audience

Academics, researchers, healthcare professionals, and policymakers across artificial intelligence, biomedical engineering, computational neuroscience, and healthcare informatics, seeking insights into brain-inspired AI and neuromorphic systems.

Jyotir Moy Chatterjee is an Assistant Professor in the Department of Computer Science and Engineering, Graphic Era University, Dehradun, India, and an Assistant Professor in the Department of Information Technology at the Lord Buddha Education Foundation, affiliated with the Asia Pacific University of Technology and Innovation, Malaysia. He has more than 100 publications to his credit, including book chapters and articles in international journals and conferences. He has edited multiple volumes. His research interests focus on machine learning and deep learning.