Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part II External validation.

Artificial intelligence (AI) is emerging as a valuable diagnostic tool in veterinary medicine, offering affordable and accessible tests that can match or even exceed the performance of medical professionals in similar tasks. Despite the promising outcomes of using AI systems (AIS) as highly accurate diagnostic tools, the field of quality assurance in AIS is still in its early stages. Our Part I manuscript focused on the development and technical validation of an AIS.

Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development.

Artificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging from cell enumeration and cancer detection to prognosis forecasting, virtual staining techniques, and individually tailored treatment plans. Preclinical testing and validation of AI systems (AIS) are critical to ensure diagnostic safety, efficacy, and dependability. In this two-part series, challenges such as the AI chasm (ie, the discrepancy between the AIS model performance in research settings and real-world applications) and ethical considerations (data privacy, algorithmic bias) are reviewed and underscore the importance of tailored quality assurance measures that address the nuances of AI in veterinary pathology.

Initial development of a rapid, portable, stall-side ELISA for the measurement of equine adrenocorticotropic hormone.

Pituitary pars intermedia dysfunction (PPID) is a neurodegenerative disease of senior horses. Loss of dopaminergic inhibition of the melanotropes of the pars intermedia leads to increased concentrations of pro-opiomelanocortin (POMC)-derived peptides. Diagnosis is challenging due to pre-analytical variables, such as sample storage, handling, and time to analysis.

Impact of regression modeling on the assessment and harmonization of a point-of-care analyzer and commercial laboratory analyzer for feline plasma biochemistry testing.

Background: Regression describes the relationship of results from two analyzers, and the generated equation can be used to harmonize results. Point-of-care (POC) analyzers cannot be calibrated by the end user, so regression offers an opportunity for calculated harmonization. Harmonization (uniformity) of laboratory results facilitates the use of common reference intervals and medical decision thresholds.

Repeat patient Testing-Quality control: An example of implementation in a network of commercial laboratories.

The theory and calculations underpinning Repeat Patient Testing-Quality Control (RPT-QC) have been presented in prior publications. This paper gives an example of the process used for implementing RPT-QC in a network of veterinary commercial reference laboratories and the stages associated with the transition to the sole use of RPT-QC. To employ RPT-QC in this commercial laboratory network, eight stages of implementation were identified: (1) education, (2) data collection, (3) calculations, (4) QC recording and documentation, (5) running RPT-QC in parallel with a commercially available quality control material (QCM), (6) development of a Standard Operating Procedure (SOP), (7) development of complementary aspects supporting RPT-QC, and (8) sole use of RPT-QC.