Diagnostic Accuracy

This study evaluates the diagnostic performance of a multimodal artificial intelligence (AI) system (AIMD.1) using de-identified medical images and semi-synthetic patient simulations. The study combines retrospective analysis of existing publicly available image datasets with prospective data collection from licensed clinicians who complete diagnostic evaluation tasks. In the One-Shot Vision Differential Evaluation (OSVDE) stage, clinicians review individual de-identified medical images and generate a ranked list of potential diagnoses based solely on visual features. In the Multi-Step Conversational Non-Inferiority Evaluation (MSCNE) stage, clinicians complete diagnostic assessments using semi-synthetic patient simulations derived from de-identified medical images. Clinician performance will be compared with the AI system on the same diagnostic tasks. Human participants consist solely of licensed clinicians who provide diagnostic responses. Medical images and simulated cases are study materials and are not considered study participants. No identifiable patient data are used, and the AI system is evaluated in an offline research environment and is not used for clinical decision-making or patient care.

The present study aims to evaluate the diagnostic accuracy of fluorescent starch nanoparticles caries detection rinse versus the International Caries Detection and Assessment System (ICDAS) in identifying early dental caries among children from 6 to 11 years old.

A prospective observational diagnostic study will be conducted to assess the sensitivity and specificity of using capnography in detecting the correct placement of nasogastric tubes using the reference standards of radiography and measurement of aspirates for pH value.