AI-Powered Point-of-Care Testing: Transforming Lateral Flow, Microscopy, and Haematology
This review explores the application of artificial intelligence in point-of-care testing, focusing on lateral flow immunoassay interpretation, bright-field microscopy automation, and haematology analysis. AI-enhanced POCT addresses critical challenges in rural and resource-limited settings where trained laboratory staff and quality assurance protocols are scarce. The authors demonstrate how machine learning algorithms can improve diagnostic accuracy and consistency at the point of care.
The original study
Artificial Intelligence in Point-of-Care Testing.
- Authors
- Khan AI, Khan M, Khan R
- Journal
- Annals of laboratory medicine
- Type
- Journal Article, Review
- PMID
- 37080740
Original abstract
With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability of hospitals, lack of trained and skilled staff performing tests, and poor compliance with quality assurance protocols. Point-of-care testing using artificial intelligence (AI) is poised to be able to address these challenges. In this review, we highlight some key areas of application of AI in point-of-care testing, including lateral flow immunoassays, bright-field microscopy, and hematology, demonstrating this rapidly expanding field of laboratory medicine.