Large Language Models to Aid Gynecological Oncology Treatment (EASING)
Medical Students and Their Perception of Large Language Models (LLMs) in Gynecologic Oncology
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
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
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Sebastian Griewing, MD PhD
- Phone Number: 0049 06421 586 2589
- Email: s.griewing@uni-marburg.de
Study Contact Backup
- Name: Johannes Knitza, MD PhD
- Email: knitza@uni-marburg.de
Study Locations
-
-
-
Marburg, Germany, 35043
- Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps University Marburg
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Medical students having started with clinical subjects
Exclusion Criteria:
- Not being a medical student
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Crossover Assignment
- Masking: Single
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Other: Local language model first
Group will be given access to local language model first after using ChatGPT
|
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
|
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
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
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
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Sebastian Griewing, MD PhD, Philipps University Marburg
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
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)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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