Reasoning Enrichment With Feedback From IA in NEphrology Trial

NCT07352475 | Recruiting

• 1 other identifier: CHUL-191125
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to learn how artificial intelligence (AI) may help doctors make diagnoses in kidney medicine. The researchers want to know whether an AI tool called a large language model (LLM) can help doctors choose the correct diagnosis more often and feel more confident in their answers. Before starting the study, the research team tested several AI models and chose one of the best performers, a GPT-5-class model set to use high reasoning effort. The main questions this study aims to answer are:

1. 1.Do doctors make more correct diagnoses when they can see AI suggestions?
2. 2.Does seeing AI suggestions change how confident doctors feel about their diagnosis?
3. 3.Read a short medical scenario
4. 4.Suggest up to three possible diagnoses

Conditions

Diagnosis; Clinical Decision-making; Decision Support Systems, Clinical; Artificial Intelligence (AI) in Diagnosis

Keywords

Large Language Model (LLM); Generative AI; Diagnostic Accuracy; Clinical Vignettes; Online Study; Randomized Controlled Trial; Nephrology Diagnosis; AI Clinical Decision Support; Human-AI Collaboration; Medical Reasoning

Outcome Measures

Primary Outcomes (1)

• Final diagnostic accuracy (top-3) with vs without AI support

  For each participant, proportion of vignettes where the correct main diagnosis is included in the participant's final top-3 diagnoses. Compare final top-3 accuracy between the AI arm (after AI suggestions) and the control arm (no AI). Percentage of correctly diagnosed cases (top-3).

  From first vignette answered until the end of the study (up to 12 months).

Secondary Outcomes (12)

• Final diagnostic accuracy (top-1) with vs without AI support

  From first vignette answered until the end of the study (up to 12 months).

• Change in top-3 diagnostic accuracy before vs after AI suggestions (AI arm only)

  From first vignette answered until the end of the study (up to 12 months).

• Change in top-1 diagnostic accuracy before vs after AI suggestions (AI arm only)

  From first vignette answered until the end of the study (up to 12 months).

• Diagnostic confidence (0-10) before AI suggestions: Control vs AI arm

  From first vignette answered until the end of the study (up to 12 months).

• Final diagnostic confidence (0-10) after AI suggestions: Control vs AI arm

  From first vignette answered until the end of the study (up to 12 months).

Study Arms (2)

Group with AI suggestions

EXPERIMENTAL

Participants in this arm will complete the same clinical case vignettes as the control group. For each case, they will receive a suggested diagnosis generated by a large language model (GPT-5, high-reasoning configuration), which was selected after internal benchmarking. Participants can review the AI suggestion before entering their own final diagnostic answer. No additional information, prompts, or coaching is provided. The intervention consists solely of displaying the AI-generated diagnostic suggestion during the case-solving task.

Other: AI suggestion

Group without AI suggestions

NO INTERVENTION

Participants in this arm will complete the clinical case vignettes independently, without any AI-generated diagnostic suggestions. They will read each vignette and provide their own diagnostic answer based solely on the information presented. No external decision support or additional materials are provided.

Interventions

AI suggestion OTHER

This intervention consists of displaying an AI-generated diagnostic suggestion during the clinical case-solving task. After reading each vignette, participants see the top diagnostic proposal produced by a large language model (GPT-5, high-reasoning configuration), selected after internal benchmarking. The AI suggestion appears once per vignette and cannot be requested again or modified. Participants may revise their diagnostic answer after viewing the suggestion, but they cannot return to the vignette later. No additional guidance, coaching, or interactive features are provided.

Group with AI suggestions

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Adults aged 18 years or older.
• Able to read and answer clinical vignettes in English or French.
• Access to a computer or smartphone with an internet connection.
• Provides informed consent online.
• Participants are expected to have at least basic medical training (e.g., medical students, residents, fellows, or practicing clinicians), although no formal verification is required.

You may not qualify if:

• Individuals under 18 years of age.
• Inability to complete online study procedures.
• Prior involvement in the design, development, or evaluation of the AI system used in this study.

Sponsors & Collaborators

• University Hospital, Lille: lead
• Institut Pasteur de Lille: collaborator
• Lille University: collaborator

Study Sites (1)

Lille University Hospital (online study)

Lille, 59000, France

RECRUITING

MeSH Terms

Conditions

Disease

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Raphaël BENTEGEAC, MD, MPH

CONTACT

+33651204000; raphael.bentegeac@univ-lille.fr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr

Study Record Dates

• First Submitted: November 19, 2025
• First Posted: January 20, 2026
• Study Start: November 20, 2025
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: January 20, 2026

Record last verified: 2026-01

Locations

FR (1)