Evaluation of a Retrieval Augmented Large Language Model as a Diagnostic Copilot in Rheumatology

NCT ID: NCT07166692

Last Updated: 2025-09-10

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

82 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-10-02

Study Completion Date

2026-02-02

Brief Summary

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This trial evaluates whether providing physicians with access to Prof. Valmed, a clinical decision support medical product, improves identification of rheumatic diseases and formulation of differential diagnoses compared with conventional decision support.

Detailed Description

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Advanced AI, particularly large language models, shows promise for enhancing clinical reasoning, yet most systems such as ChatGPT are not certified as medical products. Prof. Valmed is a clinical decision support medical product designed to assist physicians in diagnostic decision making. Given frequent referral problems and diagnostic delays in rheumatology, evaluating such support is highly relevant for clinical workflows.

This randomized controlled trial will test whether access to Prof. Valmed improves physicians' diagnostic performance in cases of suspected rheumatic disease compared with conventional decision support. Participants will be randomized to either use Prof. Valmed or rely on conventional tools while working through standardized clinical cases. For each case, participants will submit up to three differential diagnoses and a confidence rating. Independent reviewers, blinded to group allocation, will adjudicate accuracy. Findings will clarify the benefits and limitations of integrating Prof. Valmed into routine practice.

Conditions

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Diagnosis

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Outcome Assessors
The evaluation of responses will be performed by assessors blinded to participant identity and treatment assignment.

Study Groups

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Prof. Valmed

Group will be given access to Prof. Valmed.

Group Type EXPERIMENTAL

Prof. Valmed

Intervention Type OTHER

Prof Valmed. decision support system.

Control group

Group will not be given access to Prof. Valmed but will be encouraged to use any resources they wish besides large language models (UpToDate, etc).

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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Prof. Valmed

Prof Valmed. decision support system.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Participants must be licensed physicians.
* Training in rheumatology, internal medicine, emergency medicine, family medicine, dermatology or orthopedics.

Exclusion Criteria

* Not currently practicing clinically.
Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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University Medical Center Hamburg-Eppendorf (UKE)

UNKNOWN

Sponsor Role collaborator

Oslo University Hospital

OTHER

Sponsor Role collaborator

Diakonhjemmet Hospital

OTHER

Sponsor Role collaborator

University Hospital Erlangen

OTHER

Sponsor Role collaborator

Charite University, Berlin, Germany

OTHER

Sponsor Role collaborator

Rheumazentrum Ruhrgebiet

OTHER

Sponsor Role collaborator

University of Lausanne Hospitals

OTHER

Sponsor Role collaborator

Klinikum Fulda

OTHER

Sponsor Role collaborator

Philipps University Marburg

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Johannes Knitza, MD PhD

Role: PRINCIPAL_INVESTIGATOR

University Marburg

Locations

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Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps University Marburg

Marburg, , Germany

Site Status

Countries

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Germany

Central Contacts

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Johannes Knitza, MD PhD MHBA

Role: CONTACT

0049 06421 586 2589

Facility Contacts

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Johannes Knitza, MD PhD MHBA

Role: primary

+49 (0)6421 586 2589

Other Identifiers

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24-221-1

Identifier Type: -

Identifier Source: org_study_id

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