Year: 2021
Type: article
Abstract: Machine learning (ML) has emerged as a powerful tool in the healthcare industry, particularly in the automation of diagnostic processes. This paper explores the integration of ML techniques in medical diagnostics, focusing on their role in automating disease identification, predicting patient outcomes, and improving the accuracy of medical diagnoses. The paper discusses various ML algorithms and their applications in clinical practice, highlighting the potential for enhancing efficiency and reducing human error. Moreover, we explore the challenges associated with implementing ML systems in healthcare, including data privacy concerns and model interpretability.
DOI: 10.71465/ajml2863
Author: Joann G. Elmore
Institution: UCLA Health
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7147240852
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