Large Language Models to Aid Gynecological Oncology Treatment (EASING)

August 25, 2025 updated by: Philipps University Marburg

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

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Estimated)

68

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Germany
      • Marburg, Germany, 35043
        • Recruiting
        • Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps University Marburg
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

- Medical students having started with clinical subjects

Exclusion Criteria:

- Not being a medical student

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Treatment
  • Allocation: Randomized
  • Interventional Model: Crossover Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Local language model first
Group will be given access to local language model first after using ChatGPT
Other: Local language model
Group will be given access to local language model first after using ChatGPT and then will get access to pdf file
Other: Guideline pdf first
Group will be given access to guideline pdf first after using ChatGPT
Other: Guideline pdf
Group will be given access to pdf file after ChatGPT and then to a local language model

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Treatment concordance with tumor board decisions
Time Frame: directly (within 10 minutes) after Intervention
Participants in each group select treatment modalities for case vignettes
directly (within 10 minutes) after Intervention

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Treatment confidence
Time Frame: directly (within 10 minutes) after Intervention
For each case participants will be asked for their treatment confidence (VAS 0-10). The mean score will be compared between decision support groups.
directly (within 10 minutes) after Intervention
Time spent for treatment decision
Time Frame: directly (within 10 minutes) after Intervention
Time (in seconds) participants spend per case between the decision support groups will be compared.
directly (within 10 minutes) after Intervention

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Philipps University Marburg

Investigators

  • Principal Investigator: Sebastian Griewing, MD PhD, Philipps University Marburg

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 2, 2025

Primary Completion (Estimated)

August 30, 2025

Study Completion (Estimated)

September 1, 2025

Study Registration Dates

First Submitted

February 11, 2025

First Submitted That Met QC Criteria

March 3, 2025

First Posted (Actual)

March 10, 2025

Study Record Updates

Last Update Posted (Estimated)

August 26, 2025

Last Update Submitted That Met QC Criteria

August 25, 2025

Last Verified

August 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 25-29 ANZ (Other Identifier: Philipps University Marburg)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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