About this document
A Taxonomical Review of Machine Learning Paradigms in Medical Care by srmcs.exam is a document available to read on EtoBox.
The document presents a taxonomical review of machine learning (ML) paradigms in healthcare, focusing on their evolution, applications, and challenges from 2015 to 2024. It categorizes ML methods into five groups: symbolic, sub-symbolic, hybrid, federated learning, and transformer-based approaches, while addressing issues like bias, ethical governance, and the need for operational AI in clinical settings. The review emphasizes the importance of federated learning and machine learning operations in enhancing
- Author
- srmcs.exam
- Language
- EN