ARTIFICIAL INTELLIGENCE IN THE INTERPRETATION OF COMPLETE BLOOD COUNTS
DOI:
https://doi.org/10.63330/aurumpub.034-002Keywords:
Artificial Intelligence, Clinical pathology, Complete blood count, Laboratory diagnosis, Machine learningAbstract
The application of artificial intelligence (AI) in the interpretation of complete blood counts represents a significant advance in laboratory and clinical practice by enabling faster, more accurate, and standardized analysis of hematological parameters. The objective of this chapter is to discuss the role of AI in supporting diagnosis based on blood count data, highlighting its benefits, limitations, and future perspectives. The methodology consists of a narrative review of the scientific literature, drawing on studies by authors such as Goodfellow et al., Esteva et al., and Topol, who investigate the use of machine learning algorithms and neural networks in biomedical data analysis. The results indicate that AI-based systems can identify hematological patterns associated with anemia, infections, leukemia, and inflammatory disorders with high sensitivity and specificity, while also reducing human error and laboratory turnaround time. However, challenges remain regarding data quality, algorithm interpretability, and integration with healthcare systems. It is concluded that artificial intelligence is a promising tool for blood count interpretation, contributing to clinical decision-making when ethically applied, properly validated, and integrated with professional expertise.
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