Large Language Models to Aid Gynecological Oncology Treatment
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Local language model, Guideline pdf.
- Who it may be relevant to
- Registry conditions: Breast Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Germany
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Medical Students and Their Perception of Large Language Models (LLMs) in Gynecologic Oncology
Overview
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.
Interventions
- 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
Group will be given access to pdf file after ChatGPT and then to a local language model
Primary outcome measures
- Treatment concordance with tumor board decisions [Time frame: directly (within 10 minutes) after Intervention]
Secondary outcome measures (2)
- Treatment confidence [Time frame: directly (within 10 minutes) after Intervention]
- Time spent for treatment decision [Time frame: directly (within 10 minutes) after Intervention]
Eligibility criteria
Inclusion criteria
\- Medical students having started with clinical subjects
Exclusion criteria
\- Not being a medical student
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Crossover
- Masking
- Single blind
- Primary purpose
- Treatment
Study locations
Germany · 1 center
- Institute for Digital Medicine, University Hospital of Giessen and Marburg, Philipps Unive — Marburg
Identifiers
NCT: NCT06865534 · 25-29 ANZ · 25-29 ANZ