A Taxonomical Review of Machine Learning Paradigms in Medical Care

Abstract

Machine Learning (ML) has become a revolutionary technology in modern healthcare, enabling data-driven identification and enhancement of clinical decision-making. This paper reviews the research on ML from 2015 to 2024. Healthcare will focus on evolution, domain-specific applications, and new paradigms. The studied works are methodically categorized into five distinct groups: symbolic, Sub-symbolic, hybrid, federated learning structures, and transformer-based methods. Each paradigm is examined representative algorithms and clinical relevance, alongside trends in the performance of diagnostics, medical imaging, and physiological signal analysis. The review also examines the key points and issues related to bias, ethical implementation, and understanding of Large-scale clinical, mitigation, and data governance that hinder the feasibility of large-scale clinical trials. Specific attention is given to the recent advances like federated learning, federated learning and Machine Learning Operations, and Vision Transformers (ViTs), and reproducible artificial intelligence in healthcare. By consolidating current Promote and map out the prospective developments, this paper will provide a solution to the main task of moving ML to high-performance predictive models to clinically integrated, trustworthy and regulation adherent systems by consolidating current trends and mapping future developments. The review is concerned with the specifics of the paradigm design of Federated Learning and MLOps to overcome the challenges of privacy, ethical governance, and the ability to implement it in practice in the healthcare sector.

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