The expanding role of Artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging.
Authors
Affiliations (8)
Affiliations (8)
- Production Department, Carayakhteh Co. (Ltd), Tehran, Iran.
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
- Social Determinants of Health Research Center, Semnan University of Medical Sciences, Semnan, Iran.
- Molecular and Medicine Research Center, Arak University of Medical Sciences, Arak, Iran.
- Department of Clinical Biochemistry, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
- Biochemistry Department, Guilan University of Medical Sciences, Rasht, Iran.
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran.
- ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
Abstract
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). 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. 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.