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OpenTrials
Completed

NCT Number: NCT06748170

Al to Improve the Diagnosis of Rare Rheumatic Diseases

This trial aims to assess the impact of providing medical students with access to ChatGPT, a state-of-the-art large language model, in comparison to conventional diagnostic decision support tools, on their diagnostic accuracy for rare rheumatic diseases.

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Key information

Conditions

Rheumatic Diseases Connective Tissue Diseases Musculoskeletal Diseases Skin and Connective Tissue Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

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

Marburg, 35043, Germany

About this study

Advanced artificial intelligence (AI) technologies, particularly large language models such as OpenAI's ChatGPT, hold significant potential for enhancing medical decision-making. While ChatGPT was not specifically designed for medical applications, it has shown utility in various healthcare scenarios, including answering patient inquiries, drafting medical documentation, and aiding consultations. Despite these advancements, its role in supporting diagnostic reasoning-especially among less experienced medical students-and for complex rare diseases remains underexplored.

Diagnostic reasoning is a multifaceted process that combines pattern recognition, knowledge synthesis, and probabilistic thinking. Tools like ChatGPT could potentially alleviate cognitive burden, enhance diagnostic accuracy, and ultimately accelerate the diagnosis for rare diseases. However, ChatGPT is not tailored for diagnostic reasoning and lacks comprehensive validation in this domain. Additionally, it is susceptible to generating misinformation or plausible-sounding but inaccurate responses, which may hinder rather than support clinical decision-making. Therefore, understanding how medical students utilize such AI tools is essential before they are integrated into educational or clinical workflows. This study will also assess a standardized prompt to facilitate ChatGPT usage and will give students direct access to enable a realistic scenario.

This study will investigate the impact of ChatGPT on the diagnostic accuracy of medical students when tackling cases of rare rheumatic diseases. Participants will be randomized into two groups: one with access to ChatGPT and one using conventional diagnostic tools. Each participant will analyze diagnostic cases by providing up to 5 differential diagnoses and and rating the diagnostic confidence. Independent reviewers, blinded to group allocation, will evaluate the accuracy and quality of their responses. This study hence aims to provide insights into the potential benefits and limitations of integrating AI tools like ChatGPT.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Medical students having started with clinical subjects (Internal medicine)

Exclusion criteria

  • Not being a medical student

Treatment and study plan

ChatGPT

Other

OpenAI's ChatGPT language model with chat interface.

Primary outcomes

  1. Diagnostic accuracy of top diagnosis

    Time frame: during evaluation

    Participants in each group will make at least one disease suggestion (top diagnosis) and up to a total of a maximum of 5 suggestions. Percentage of exact matches of the top suggestion with the actual diagnosis will be analyzed

Secondary outcomes

  1. Diagnostic accuracy of top 5 suggestions

    Time frame: during evaluation

    Participants in each group will make at least one disease suggestion (top diagnosis) and up to a total of a maximum of 5 suggestions. Percentage of exact matches with the actual diagnosis included in the top 5 suggestions will be analyzed

  2. Diagnostic reasoning

    Time frame: during evaluation

    For each case, participants will receive 1 point for each plausible diagnosis and 2 points for a completely correct response. The total scores will be compared between the randomized groups.

  3. Diagnostic confidence

    Time frame: during evaluation

    For each case participants will be asked for their diagnostic confidence (VAS 0-10). The mean score will be compared between groups.

  4. Time spent for diagnosis

    Time frame: during evaluation

    We will compare how much time (in seconds) participants spend per case between the two study arms.

Sponsors and collaborators

Lead sponsor

Philipps University Marburg

Other

Registry information

Acronym: AIDRARER

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Dec 27, 2024
Registry last updated
May 29, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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