ABSTRACT
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.
ABSTRACT
The most frequent extrathyroidal Graves' disease manifestation is Graves' orbitopathy (GO). The treatment of GO is determined by its severity and activity. There is currently no reliable, impartial method for assessing it clinically or distinguishing fibrosis from active inflammatory disorders. Today, imaging methods including orbital ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) are frequently employed to show pathological abnormalities in the ocular adnexa of GO patients. In addition, a not widely accepted technique - 99mTc-DTPA SPECT - has some potential to evaluate retrobulbar inflammation in GO patients. However, FDG-PET/CT is possibly superior to other imaging modalities in detecting inflammation in GO and it may be useful in assessing disease activity in case of clinical or serological uncertainty. It might also act as an early indicator of GO development and its aggravation before irreversible tissue alterations take place and may be used in the differential diagnosis of inflammatory disorders of the orbit. However, before FDG-PET/CT could be applied in daily clinical practice, the methodology of GO activity assessment with defined cut-off values for radionuclide concentration - standardized units of value (SUV) have to be established and validated. In addition, the limitations of this technique have to be recognized.