Machine Learning In Healthcare

Authors: Poonam Sharma, Nimi Gautam

Abstract: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved the ability to detect health emergencies, analyze disease patterns, and understand patient conditions and immune responses. Despite these advancements, there are still concerns about how reliably these models can be applied and interpreted in real-world healthcare environments. However, the adoption of ML-based solutions is growing rapidly. This paper presents an overview of key machine learning methods, including supervised, unsupervised, and reinforcement learning, along with their practical examples. It also explores the use of these techniques in various healthcare domains such as radiology, genomics, electronic health records, and neuro imaging. In addition, the study highlights major challenges, including data privacy and ethical issues, and discusses potential directions for future research and applications.

DOI: http://doi.org/10.5281/zenodo.20178263