Distributed Technologies Using AI/ML Techniques for Healthcare Applications
B. Gopi, M. L. Sworna Kokila, Christopher V. Bibin, D. Sasikala, Eric Howard, Sampath Boopathi
Abstract
The healthcare sector has benefited greatly from the integration of AI/ML with distributed technologies like edge computing, blockchain, and Internet of Things (IoT) to address challenges like data interoperability, security, and scalability. This synergy has a major impact on patient care, medical research, and the efficiency of the healthcare system. AI/ML techniques are used in a variety of fields, including drug development, medical imaging interpretation, picture identification, predictive analytics, and sickness prediction. The relationship between AI/ML and distributed technologies—such as decentralized architectures for safe access to real-time data sources, blockchain for data integrity and privacy, and edge computing for low-latency processing—is discussed. When combining AI/ML with dispersed technology, the healthcare business faces trends and concerns related to interoperability, legal compliance, and ethical issues.