PT - JOURNAL ARTICLE AU - Mohammed A. Alaidarous TI - The emergence of new trends in clinical laboratory diagnosis AID - 10.15537/smj.2020.11.25455 DP - 2020 Nov 01 TA - Saudi Medical Journal PG - 1175--1180 VI - 41 IP - 11 4099 - http://smj.org.sa/content/41/11/1175.short 4100 - http://smj.org.sa/content/41/11/1175.full SO - Saudi Med J2020 Nov 01; 41 AB - Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.