Ovarian Cancer Screening and AI (AI-OCS-Gyn)

March 25, 2026 updated by: Odette Wegwarth, Charite University, Berlin, Germany

AI on Ovarian Cancer Screening Attitudes in Gynecologists

Gynecologists frequently overestimate the benefits and safety of ovarian cancer screening. AI-supported discussions may help correct these misperceptions. This study tests whether an AI-guided conversation about the evidence on ovarian cancer screening can improve gynecologists' knowledge and reduce non-evidence-based screening recommendations, compared with a control AI discussion on ovarian cancer prevalence.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Previous research has demonstrated that gynecologists often substantially overestimate both the effectiveness and safety of ovarian cancer screening, despite robust evidence indicating that such screening does not offer a net clinical benefit. These findings highlight the need for innovative communication strategies to support evidence-based clinical practice and reduce low value care.

AI-based conversational interventions have shown promising results in other fields when aiming to correct misconceptions or encourage engagement with evidence, particularly among individuals who are initially resistant to factual information. Leveraging these insights, this study investigates whether AI-facilitated discussions can effectively improve gynecologists' knowledge of the benefit-harm profile of ovarian cancer screening and subsequently reduce non-evidence-based recommendations.

The study employs a cross-sectional study design in which gynecologists who have previously indicated to regularly recommend ovarian cancer screening with transvaginal ultrasound and potentially with additional CA 125-testing to their asymptomatic, average-risk patients are randomized to one of two conditions:

  1. Intervention Condition: Participants engage in an AI-guided conversation in which they explain their reasons for recommending ovarian cancer screening. The AI is instructed to address misconceptions and clarify the lack of evidence supporting a positive benefit-harm ratio.
  2. Control Condition: Participants engage in an AI discussion on the prevalence of ovarian cancer, without receiving information or corrective feedback related to screening outcomes.

Before and after the AI-based discussion, all participants are queried on their numerical (X out of 1,000 women) and subjective perception of ovarian cancer screening's benefits and harms and their screening recommendations. Measures are derived from instruments used in prior research.

The primary objective of this study is to assess the change, from before to after the AI-based conversation, in clinicians' understanding of the benefit-harm ratio and their recommendations regarding routine ovarian cancer screening for asymptomatic, average-risk women, within and between study groups.

Study Type

Interventional

Enrollment (Estimated)

350

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
    • State of Berlin
      • Mitte, State of Berlin, Germany, 10117
        • Charité - Universitätsmedizin Berlin

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

No

Description

Inclusion Criteria:

  • gynecologists in outpatient care who provide ovarian cancer screening to asymptomatic, average-risk women (not guideline consistent)

Exclusion Criteria:

  • gynecologists in inpatient care
  • gynecologist in outpatient care who do NOT provide ovarian cancer screening to asymptomatic, average-risk women (guideline consistent)

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: Screening
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Control (ChatGPT Control Condition)

Participants in this arm engage in a three-turn conversation with ChatGPT. The AI's role is to:

  • Discuss the participant's perception of how dangerous ovarian cancer is.
  • Provide factual information on prevalence, lifetime risk, mortality rates, and general epidemiology.
  • Avoid any mention of screening tests, guideline recommendations, or screening benefits/harms.
  • Keep responses concise (5-8 sentences per turn).
  • Begin by reacting to the participant's opening question: "In your mind, how dangerous is ovarian cancer?"
Behavioral: ChatGPT - Control

Three-turn conversation; discusses ovarian cancer risk and epidemiology; avoids screening topics; concise responses (5-8 sentences).

Mode of Delivery: Online chat interface; participant interacts directly with ChatGPT.
Experimental: Experimental (ChatGPT Evidence-Based Screening Discussion)

Participants in this arm engage in a three-turn conversation with ChatGPT. The AI's role is to:

  • Ask participants to elaborate on their reasons for recommending ovarian cancer screening.
  • Provide clear, evidence-based information about benefits and harms of ovarian cancer screening in average-risk women.
  • Refer to key findings from large trials (e.g., PLCO, UKCTOCS) with absolute numbers (false-positive rates, unnecessary surgeries, complication rates, lack of mortality benefit).
  • Summarize positions of major U.S. guidelines (e.g., USPSTF, ACOG), including recommendation against routine screening in asymptomatic, average-risk women.
  • Evaluate the evidence and state whether routine screening is supported based on current data.
  • Maintain a respectful, non-judgmental tone; critique evidence, not the participant.
  • Keep responses concise (5-8 sentences per turn).
  • Begin by responding to the participant's opening question: "Why do you recommend ovarian cancer screening?"
Behavioral: ChatGPT - Evidence-Based Screening Discussion

Three-turn conversation; asks participants about screening rationale; provides evidence-based info on benefits/harms, trial data, guideline positions; concise responses (5-8 sentences).

Mode of Delivery: Online chat interface; participant interacts directly with ChatGPT.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in intention to recommend ovarian cancer screening
Time Frame: Immediately post intervention
Difference in participants' self-reported frequency of recommending ovarian cancer screening to average-risk women in the future after the ChatGPT interaction and their self-reported frequency of recommending the screening in the past.
Immediately post intervention

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in benefit-harm ratio evaluation of ovarian cancer screenings
Time Frame: Immediately post intervention
Difference between the self-reported benefit-harm ratio evaluation before and after the ChatGPT interaction.
Immediately post intervention
Accuracy of knowledge regarding ovarian cancer screening evidence
Time Frame: Immediately post intervention
Participants' understanding of benefits, harms, and guideline recommendations for ovarian cancer screening, assessed via survey questions
Immediately post intervention

Collaborators and Investigators

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

Sponsor

Charite University, Berlin, Germany

Collaborators

German Research Foundation
Max Planck Institute for Human Development

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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 (Estimated)

March 27, 2026

Primary Completion (Estimated)

April 30, 2026

Study Completion (Estimated)

April 30, 2026

Study Registration Dates

First Submitted

March 25, 2026

First Submitted That Met QC Criteria

March 25, 2026

First Posted (Actual)

March 31, 2026

Study Record Updates

Last Update Posted (Actual)

March 31, 2026

Last Update Submitted That Met QC Criteria

March 25, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 2025ChatGPTGyn

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

We will use osf.io to publish the raw behavioral data of the participants without any identifiers and open text communications after study completion and publishing of results (October 2026).

IPD Sharing Supporting Information Type

  • ANALYTIC_CODE

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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