Rheumatic DiseasesAIDRARERNCT06748170
Al to Improve the Diagnosis of Rare Rheumatic Diseases
A study testing ChatGPT in people with Rheumatic Diseases, run by Philipps University Marburg at 1 site in Germany.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Rheumatic Diseases
- What is being tested?
- ChatGPT
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 68 participants
- Where
- 1 siteGermany
- Study dates
- 7 Jan 2025to 30 Mar 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Medical students having started with clinical subjects (Internal medicine)
Who cannot join
- Not being a medical student
Trial locations and contacts
1 site in 1 country.
Germany1 site
Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps University Marburg
Marburg, 35043
About this study
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.
Read the detailed description
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.
- Conditions
- Rheumatic Diseases
- Being tested
- ChatGPT
- Keywords
- Large language models, Diagnostic decision support, Clinical reasoning, rheumatology
- Medical subject headings
- Rheumatic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ChatGPTOther
OpenAI's ChatGPT language model with chat interface.
| Study group | Role | What they receive |
|---|---|---|
| Intervention group | Active comparator | Group will be given access to ChatGPT and standardized initial prompt Receives: Other: ChatGPT |
| Control group | No intervention | Group will not be given access to any LLMs including ChatGPT but will be motivated to use other resources (such as online search enginges, Pubmed) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of top diagnosis
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
Measured: during evaluation
Secondary outcomes (4)
Diagnostic accuracy of top 5 suggestions
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
Measured: during evaluation
Diagnostic reasoning
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.
Measured: during evaluation
Diagnostic confidence
For each case participants will be asked for their diagnostic confidence (VAS 0-10). The mean score will be compared between groups.
Measured: during evaluation
Time spent for diagnosis
We will compare how much time (in seconds) participants spend per case between the two study arms.
Measured: during evaluation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Dec 2024
- Study start7 Jan 2025
- Primary completion30 Mar 2025
- Study completion30 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Rheumatic DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- The trial will be designed as a randomized, two-arm, single-blind parallel group study.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 68 participants (actual)
Sponsor and investigators
- Lead sponsor
- Philipps University Marburg
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06748170
- Sponsor's protocol ID
- 24-221 ANZ
- Acronym
- AIDRARER
- Status last verified
- May 2025
- First submitted
- 16 Dec 2024
- Last updated
- 29 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Roemer A, Schlicker N, Kernder A, Albe B, Hack J, Hirsch M, Mayr A, Kuhn S, Knitza J. Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial. BMC Med Educ. 2026 Mar 25;26(1):579. doi: 10.1186/s12909-026-09079-w.
PubMed 41877128
Study at a glance
From the registry record, last updated by the sponsor on 29 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Al to Improve the Diagnosis of Rare Rheumatic Diseases
- ClinicalTrials.gov ID
- NCT06748170
- Acronym / sponsor ID
- AIDRARER / 24-221 ANZ
- Conditions
- Rheumatic Diseases
- Keywords
- Large language models, Diagnostic decision support, Clinical reasoning, rheumatology
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 68 participants (actual)
- Lead sponsor
- Philipps University MarburgUniversity, hospital or other
- Study start
- 7 Jan 2025 (actual)
- Primary completion
- 30 Mar 2025 (actual)
- Study completion
- 30 Mar 2025 (actual)
- First posted
- 27 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- The trial will be designed as a randomized, two-arm, single-blind parallel group study.
| Arm | Type | Receives |
|---|---|---|
| Intervention group | Active comparator | Other: ChatGPTGroup will be given access to ChatGPT and standardized initial prompt |
| Control group | No intervention | –Group will not be given access to any LLMs including ChatGPT but will be motivated to use other resources (such as online search enginges, Pubmed) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Medical students having started with clinical subjects (Internal medicine)
Key exclusion criteria (1 listed)
- Not being a medical student
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ChatGPTOpenAI's ChatGPT language model with chat interface. | Other | Intervention group |
Primary outcome
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
4 secondary outcomes
Diagnostic accuracy of top 5 suggestions
Time frame: during evaluation
Diagnostic reasoning
Time frame: during evaluation
Diagnostic confidence
Time frame: during evaluation
Time spent for diagnosis
Time frame: during evaluation
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 May 2025.
- Derived from this study: PubMed 41877128Roemer A, Schlicker N, Kernder A, Albe B, Hack J, Hirsch M, Mayr A, Kuhn S, Knitza J. Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial. BMC Med Educ. 2026 Mar 25;26(1):579. doi: 10.1186/s12909-026-09079-w.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing ChatGPT in people with Rheumatic Diseases, run by Philipps University Marburg at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of top diagnosis (time frame: during evaluation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06748170, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 May 2025.