Health TrialsbyHealthPanel

Reasoning Enrichment With Feedback From IA in NEphrology Trial

ConditionsDiagnosisClinical Decision-makingArtificial Intelligence (AI) in DiagnosisDecision Support Systems, Clinical
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorUniversity Hospital, Lille

About this trial

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. Do doctors make more correct diagnoses when they can see AI suggestions? 2. Does seeing AI suggestions change how confident doctors feel about their diagnosis?

Researchers will compare doctors who receive AI suggestions with doctors who do not receive AI suggestions to see how the AI affects accuracy, confidence, and decision-making.

Participants will complete up to 10 online clinical cases. For each case, they will:

1. Read a short medical scenario 2. Suggest up to three possible diagnoses

(If in the AI group) Review the AI's suggestions and decide whether to change their answer

The study will also look at how long participants take to answer each case and how the AI's performance compares to the human answers.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

None

Disqualifiers

None

Trial design

Design model

Parallel

Treatments tested in this trial

  • AI suggestion

    Other intervention

    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.

Treatment groups

100 Participants
are divided into 2 treatment groups
Group A: Group with AI suggestionsExperimental treatment 1 intervention
Group B: Group without AI suggestionsNo intervention 0 interventions

Trial outcomes

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).

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

Secondary outcomes

1

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

For each participant, proportion of vignettes where the correct main diagnosis is included in the participant's final top-1 diagnoses. Compare final top-1 accuracy between the AI arm (after AI suggestions) and the control arm (no AI). Percentage of correctly diagnosed cases (top-1).

Time frame
From first vignette answered until the end of the study (up to 12 months).
2

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

In the AI-supported arm, participants first provide an initial answer (up to three diagnoses) without AI suggestions, then see AI-generated suggestions and may revise their answer once; they cannot return to that vignette later. For each participant, the investigators compute the difference in top-3 accuracy between initial and final answers across all completed vignettes. Percentage-point change in Top-3 diagnostic accuracy

Time frame
From first vignette answered until the end of the study (up to 12 months).
3

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

In the AI-supported arm, participants first provide an initial answer (up to three diagnoses) without AI suggestions, then see AI-generated suggestions and may revise their answer once; they cannot return to that vignette later. For each participant, the investigators compute the difference in top-1 accuracy between initial and final answers across all completed vignettes. Percentage-point change in Top-1 diagnostic accuracy

Time frame
From first vignette answered until the end of the study (up to 12 months).
4

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

Participants in both arms rate their confidence (0-10 scale) in their Top-3 diagnostic proposal before any AI suggestions. In the AI arm, this is the "pre-AI" rating. In the Control arm, this is the single confidence rating (since no AI is shown). The investigators compare the pre-AI confidence between arms, aggregated across all completed vignettes per participant.

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

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University Hospital, Lille

Lead sponsor

Institut Pasteur de Lille

Collaborator

Lille University

Collaborator

Source ClinicalTrials.gov
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