Breast CancerEASINGNCT06865534
Large Language Models to Aid Gynecological Oncology Treatment
A study testing Local language model and Guideline pdf in people with Breast Cancer, 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 large language models, in comparison to treatment guideline pdfs, on treatment concordance with a conventional multidisciplinary tumor board Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Local language model and Guideline pdf
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 70 participants
- Where
- 1 siteGermany
- Study dates
- 22 May 2026to 6 Aug 2026
- 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
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
Official title: Medical Students and Their Perception of Large Language Models (LLMs) in Gynecologic Oncology
This trial aims to assess the impact of providing medical students with access to large language models, in comparison to treatment guideline pdfs, on treatment concordance with a conventional multidisciplinary tumor board
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 clinical consultations. Despite these advancements, its role in supporting treatment decision-making-particularly in complex oncological cases-remains underexplored.
Treatment decision-making in gynecological oncology is a multifaceted process that integrates evidence-based guidelines, tumor biology, patient-specific factors, and clinical expertise. AI tools like ChatGPT could potentially assist in synthesizing relevant guideline-based recommendations, improving decision accuracy, and facilitating more efficient clinical workflows. However, ChatGPT is not specifically tailored for oncological treatment decisions and lacks comprehensive validation in this domain. Additionally, it may generate misinformation or plausible-sounding but inaccurate recommendations, which could impact clinical judgment. Therefore, understanding how medical professionals, including students and early-career physicians, interact with such AI tools is essential before broader integration into clinical practice. Locally deployable models, such as Llama, enable secure, on-premise usage while retrieval-augmented generation ensures guideline-compliant recommendations.
This study will investigate the impact of language models on treatment decision support for medical students managing gynecological oncology cases. This is a crossover study, where participants will be randomized into two groups. All participants begin with access to ChatGPT for two vignettes. They then proceed with two cases using either a locally deployed language model, followed by two cases relying on guideline PDFs, or vice versa.
Each participant will analyze clinical cases, propose treatment plans, and rate their confidence in their decisions and decision support system usability. This study aims to provide insights into the potential benefits and limitations of integrating AI tools like ChatGPT into oncological treatment decision-making.
- Conditions
- Breast Cancer
- Being tested
- Local language model, Guideline pdf
- Keywords
- large language models, clinical decision support, gynecological oncology
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Local language modelOther
Group will be given access to local language model first after using ChatGPT and then will get access to pdf file
Guideline pdfOther
Group will be given access to pdf file after ChatGPT and then to a local language model
| Study group | Role | What they receive |
|---|---|---|
| Local language model first | Other | Group will be given access to local language model first after using ChatGPT Receives: Other: Local language model |
| Guideline pdf first | Other | Group will be given access to guideline pdf first after using ChatGPT Receives: Other: Guideline pdf |
What is being measured
The primary outcome is the main question the study is designed to answer.
Treatment concordance with tumor board decisions
Participants in each group select treatment modalities for case vignettes
Measured: directly (within 10 minutes) after Intervention
Secondary outcomes (2)
Treatment confidence
For each case participants will be asked for their treatment confidence (VAS 0-10). The mean score will be compared between decision support groups.
Measured: directly (within 10 minutes) after Intervention
Time spent for treatment decision
Time (in seconds) participants spend per case between the decision support groups will be compared.
Measured: directly (within 10 minutes) after Intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Mar 2025
- Study start22 May 2026
- Primary completion6 Aug 2026
- Study completion6 Aug 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast CancerStudies 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
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 70 participants (actual)
Sponsor and investigators
- Lead sponsor
- Philipps University Marburg
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Sebastian Griewing, MD PhD, Principal investigator, Philipps University Marburg
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06865534
- Sponsor's protocol ID
- 25-29 ANZ
- Other IDs
- 25-29 ANZ
- Acronym
- EASING
- Status last verified
- Aug 2026
- First submitted
- 11 Feb 2025
- Last updated
- 19 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Medical Students and Their Perception of Large Language Models (LLMs) in Gynecologic Oncology
- ClinicalTrials.gov ID
- NCT06865534
- Acronym / sponsor ID
- EASING / 25-29 ANZ
- Conditions
- Breast Cancer
- Keywords
- large language models, clinical decision support, gynecological oncology
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 70 participants (actual)
- Lead sponsor
- Philipps University MarburgUniversity, hospital or other
- Study start
- 22 May 2026 (actual)
- Primary completion
- 6 Aug 2026 (actual)
- Study completion
- 6 Aug 2026 (actual)
- First posted
- 10 Mar 2025 (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 large language models, in comparison to treatment guideline pdfs, on treatment concordance with a conventional multidisciplinary tumor board
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 clinical consultations. Despite these advancements, its role in supporting treatment decision-making-particularly in complex oncological cases-remains underexplored.
Treatment decision-making in gynecological oncology is a multifaceted process that integrates evidence-based guidelines, tumor biology, patient-specific factors, and clinical expertise. AI tools like ChatGPT could potentially assist in synthesizing relevant guideline-based recommendations, improving decision accuracy, and facilitating more efficient clinical workflows. However, ChatGPT is not specifically tailored for oncological treatment decisions and lacks comprehensive validation in this domain. Additionally, it may generate misinformation or plausible-sounding but inaccurate recommendations, which could impact clinical judgment. Therefore, understanding how medical professionals, including students and early-career physicians, interact with such AI tools is essential before broader integration into clinical practice. Locally deployable models, such as Llama, enable secure, on-premise usage while retrieval-augmented generation ensures guideline-compliant recommendations.
This study will investigate the impact of language models on treatment decision support for medical students managing gynecological oncology cases. This is a crossover study, where participants will be randomized into two groups. All participants begin with access to ChatGPT for two vignettes. They then proceed with two cases using either a locally deployed language model, followed by two cases relying on guideline PDFs, or vice versa.
Each participant will analyze clinical cases, propose treatment plans, and rate their confidence in their decisions and decision support system usability. This study aims to provide insights into the potential benefits and limitations of integrating AI tools like ChatGPT into oncological treatment decision-making.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Local language model first | Other | Other: Local language modelGroup will be given access to local language model first after using ChatGPT |
| Guideline pdf first | Other | Other: Guideline pdfGroup will be given access to guideline pdf first after using ChatGPT |
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
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 |
|---|---|---|
| Local language modelGroup will be given access to local language model first after using ChatGPT and then will get access to pdf file | Other | Local language model first |
| Guideline pdfGroup will be given access to pdf file after ChatGPT and then to a local language model | Other | Guideline pdf first |
Primary outcome
Treatment concordance with tumor board decisions
Time frame: directly (within 10 minutes) after Intervention
Participants in each group select treatment modalities for case vignettes
2 secondary outcomes
Treatment confidence
Time frame: directly (within 10 minutes) after Intervention
Time spent for treatment decision
Time frame: directly (within 10 minutes) after Intervention
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 19 Aug 2026.
- 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.
Sebastian Griewing, MD PhD principal investigator
Philipps University Marburg
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Local language model and Guideline pdf in people with Breast Cancer, 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 19 Aug 2026. 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?
Treatment concordance with tumor board decisions (time frame: directly (within 10 minutes) after Intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 19 Aug 2026.