Clinical trial · Interventional
Large Language Models to Aid Gynecological Oncology Treatment
Medical Students and Their Perception of Large Language Models (LLMs) in Gynecologic Oncology
NCT06865534CI-TRIAL-00122668EASINGcompletedN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Guideline pdf | Other | — | UNRESOLVED |
| Local language model | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- OTHER
- label
- Local language model first
- description
- Group will be given access to local language model first after using ChatGPT
- interventionNames
- Other: Local language model
- type
- OTHER
- label
- Guideline pdf first
- description
- Group will be given access to guideline pdf first after using ChatGPT
- interventionNames
- Other: Guideline pdf
Primary outcomes (1)
- measure
- Treatment concordance with tumor board decisions
- timeFrame
- directly (within 10 minutes) after Intervention
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: \- Medical students having started with clinical subjects Exclusion Criteria: \- Not being a medical student
References
Publications (0)
Data not yet available
No reference posted for this study.