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Evaluation of One-Shot Vision Differential Diagnosis (OSVDE) and Multi-Step Conversational Non-Inferiority (MSCNE) in AI Medical Interviewing
Sponsor: Magic Health Inc. (d.b.a. Nolla Health)
Summary
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.
Official title: AI Medical Interviewing and Diagnostic System Performance Evaluation: One-Shot Vision Differential Diagnosis (OSVDE) and Multi-Step Conversational Non-Inferiority (MSCNE) Evaluation.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
30
Start Date
2026-03-19
Completion Date
2026-09-19
Last Updated
2026-03-25
Healthy Volunteers
Yes
Interventions
AI Diagnostic System (AIMD.1)
AIMD.1 (also known as NollaMD agent) is a multimodal artificial intelligence (AI) diagnostic system designed to generate differential diagnoses based on analysis of medical images and structured clinical information. In this study, the system is evaluated using de-identified medical images and semi-synthetic patient simulations under controlled research conditions. The AI system generates ranked diagnostic outputs and associated confidence scores, which are compared with reference diagnoses and clinician performance metrics. The system is evaluated in an offline research environment. AI outputs are not used for clinical decision-making, patient management, or real-world medical care.
Locations (1)
Nolla Health (Magic Health Inc.)
New York, New York, United States