The expanding role of artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging
TL;DR
Artificial Intelligence can be very effective in increasing medical professionals’ knowledge and, consequently, improving patient outcomes, but its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data.
Abstract
ABSTRACT Introduction The use of Artificial Intelligence (AI), especially Machine learning (ML) and Deep learning (DL), has led to a major shift in medical diagnosis. AI can assist medical professionals in medical diagnosis by its unique ability to analyze complex data from multiple sources, including medical images, gene sequences, and Electronic Health Records (EHRs). Areas covered Its application in other clinical processes, such as risk classification, diagnostic workflows, and disease risk prediction from patient symptoms, can also speed up diagnosis, reduce costs, and improve diagnostic outcomes. Expert Opinion However, its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data. By addressing these limitations, AI can be very effective in increasing medical professionals’ knowledge and, consequently, improving patient outcomes.