The expanding role of Artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging

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.

Authors

Institutions

Publication Details

Journal
Expert Review of Molecular Diagnostics
Published
2026-07-16
DOI
https://doi.org/10.1080/14737159.2026.2706000
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The expanding role of Artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging

Navid Pourzardosht, Fahimeh Fattahi, Faezeh Noorabad ghahroodi, Pourya Nasirmoghadas et al.
Expert Review of Molecular Diagnostics
Artificial Intelligence in Healthcare and Education
article

The expanding role of Artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging

Navid Pourzardosht, Fahimeh Fattahi, Faezeh Noorabad ghahroodi, Pourya Nasirmoghadas, Bahman Khalesi, Saeed Khalili, Zahra Sadat Hashemi, Fatemeh Mahmoudian, Aghdas Ramezani, Marzieh Bagheri
article en

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.

Expert Review of Molecular Diagnostics
Arak University of Medical Sciences (IR), Tarbiat Modares University (IR), Guilan University of Medical Sciences (IR), Motamed Cancer Institute (IR), Agricultural Research & Education Organization (IR), Semnan University of Medical Sciences (IR), Nano Carbon (Poland) (PL)
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.