JMIR Publications

ABSTRACT

Background:

In recent years, the implementation of artificial intelligence (AI) in healthcare is progressively transforming medical fields, with Clinical Decision Support Systems (CDSS) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results' significance. The potential role of CDSS in laboratory diagnostics gains significance, however, more research needs to explore this area.

Objective:

The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients' medical histories.

Methods:

This cohort study embraced a prospective data collection approach. A total of 101 patients were enrolled, aged 18 and above, in stable condition, requiring comprehensive diagnosis. A panel of blood laboratory tests was conducted for each participant. Participants utilized LabTest Checker for test result interpretation. Accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, considered the gold standard.

Results:

The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% and achieved an 82.9% accuracy in identifying underlying pathologies.

Conclusions:

This study underscores the transformative potential of AI-based CDSS in laboratory diagnostics, contributing to enhanced patient care, efficient healthcare systems, and improved medical outcomes. LabTest Checker's performance evaluation highlights the advancements in AI's role in laboratory medicine.