Machine Learning Applications In Healthcare: The State Of Knowledge and Future Directions

TL;DR

This paper reviews the current state and future prospects of machine learning applications in healthcare, categorizing them into five key areas to facilitate quick access to relevant information and promote adoption.

Contribution

It provides a structured, concise overview of ML applications in healthcare, addressing knowledge gaps and aiding healthcare professionals in understanding ML's potential.

Findings

ML has significant potential in healthcare but faces adoption barriers.

The study categorizes applications into five major groups for clarity.

Provides a comprehensive, accessible reference for healthcare ML applications.

Abstract

Detection of easily missed hidden patterns with fast processing power makes machine learning (ML) indispensable to today's healthcare system. Though many ML applications have already been discovered and many are still under investigation, only a few have been adopted by current healthcare systems. As a result, there exists an enormous opportunity in healthcare system for ML but distributed information, scarcity of properly arranged and easily explainable documentation in related sector are major impede which are making ML applications difficult to healthcare professionals. This study aimed to gather ML applications in different areas of healthcare concisely and more effectively so that necessary information can be accessed immediately with relevant references. We divided our study into five major groups: community level work, risk management/ preventive care, healthcare operation management, remote care, and early detection. Dividing these groups into subgroups, we provided relevant references with description in tabular form for quick access. Our objective is to inform people about ML applicability in healthcare industry, reduce the knowledge gap of clinicians about the ML applications and motivate healthcare professionals towards more machine learning based healthcare system.