TY  - JOUR
AU  - Szumilas, Dawid
AU  - Ochmann, Anna
AU  - Zięba, Katarzyna
AU  - Bartoszewicz, Bartłomiej
AU  - Kubrak, Anna
AU  - Makuch, Sebastian
AU  - Agrawal, Siddarth
AU  - Mazur, Grzegorz
AU  - Chudek, Jerzy
PY  - 2024
DA  - 2024/8/14
TI  - Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study
JO  - JMIR Med Inform
SP  - e57162
VL  - 12
KW  - LabTest Checker
KW  - CDSS
KW  - symptom checker
KW  - laboratory testing
KW  - AI
KW  - assessment
KW  - accuracy
KW  - artificial intelligence
KW  - health care
KW  - medical fields
KW  - clinical decision support systems
KW  - application
KW  - applications
KW  - diagnoses
KW  - patients
KW  - patient
KW  - medical history
KW  - tool
KW  - tools
AB  - Background: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) 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. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed 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 aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are 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% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies. Conclusions: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC’s performance evaluation highlights the advancements in AI’s role in laboratory medicine. Trial Registration: ClinicalTrials.gov NCT05813938; https://clinicaltrials.gov/study/NCT05813938 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2024/1/e57162
UR  - https://doi.org/10.2196/57162
DO  - 10.2196/57162
ID  - info:doi/10.2196/57162
ER  -