RT Journal Article
SR Electronic
T1 The emergence of new trends in clinical laboratory diagnosis
JF Saudi Medical Journal
JO Saudi Med J
FD Prince Sultan Military Medical City
SP 1175
OP 1180
DO 10.15537/smj.2020.11.25455
VO 41
IS 11
A1 Mohammed A. Alaidarous
YR 2020
UL http://smj.org.sa/content/41/11/1175.abstract
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.