Clinical Research Directory

Evaluation of One-Shot Vision Differential Diagnosis (OSVDE) and Multi-Step Conversational Non-Inferiority (MSCNE) in AI Medical Interviewing

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.

AI-Orchestrated Workflow Versus Consultant Ophthalmologist for Refractive Surgery and Keratoconus Diagnosis (AEYE Trial)

Background and Rationale: Laser vision correction procedures, such as LASIK (Laser-Assisted In Situ Keratomileusis), PRK (Photorefractive Keratectomy), and SMILE (Small Incision Lenticule Extraction), are highly effective but require careful preoperative screening to ensure safety. One of the most critical aspects of screening is identifying keratoconus and other corneal ectatic disorders-conditions that cause progressive thinning and bulging of the cornea, often contraindicating surgery. Early detection is essential to avoid vision-threatening complications. Despite advanced corneal imaging tools such as Scheimpflug tomography and anterior segment optical coherence tomography (AS-OCT), accurate diagnosis-particularly in borderline or early-stage cases-remains challenging and subject to variability in human interpretation. Artificial intelligence (AI) offers the potential to improve diagnostic precision, reduce oversight, and standardize surgical planning. Purpose of the Study: This study evaluates the performance of AEYE (Automated Evaluation for Your Eye), a multi-agent AI system designed to support ophthalmologists in diagnosing keratoconus and determining refractive surgery eligibility. AEYE simulates the clinical workflow of an anterior segment specialist by orchestrating three specialized agents: History \& Risk Agent: Reviews patient history and extracts risk factors. Imaging Agent: Analyzes corneal tomography, AS-OCT, and epithelial mapping scans. Surgical Decision Agent: Integrates all findings, assigns a diagnosis, and recommends appropriate treatment options, including surgical eligibility or corneal cross-linking (CXL). Study Design: The study includes 50 real-world patient cases, both retrospective (from 2020 onward) and prospective, who were evaluated for refractive surgery or keratoconus. Each case is analyzed independently by AEYE and a consultant ophthalmologist (blinded to AI output), using the same multimodal clinical and imaging data. Diagnostic accuracy, agreement in surgical recommendations, and workflow efficiency are assessed. Anticipated Impact: By comparing AI-derived decisions with expert clinical judgment, this study aims to validate whether structured AI workflows like AEYE can serve as reliable, safe, and explainable decision support tools. If successful, AEYE may offer a scalable solution to reduce diagnostic variability and enhance the safety and consistency of refractive surgery screening.

Comparing Fluorescent Starch Nanoparticles Rinse with ICDAS for Early Caries Detection in Children

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.

Diagnostic Accuracy of Capnography in Nasogastric Tube Placement

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.