Ovarian Cancer Screening Recommendations by GynecologistsAI-OCS-GynNCT07503054
Ovarian Cancer Screening and AI
A study testing ChatGPT - Evidence-Based Screening Discussion in people with Ovarian Cancer Screening Recommendations by Gynecologists, run by Charite University, Berlin, Germany at 1 site in Germany.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 24 years and older
- Conditions the study is for
- Ovarian Cancer Screening Recommendations by Gynecologists
- What is being tested?
- ChatGPT - Evidence-Based Screening Discussion
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- 1 siteGermany
- Study dates
- 27 Mar 2026to 15 Sep 2026
- Phase
- Interventional
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 24 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- gynecologists in outpatient care who provide ovarian cancer screening to asymptomatic, average-risk women (not guideline consistent)
Who cannot join
- gynecologists in inpatient care
- gynecologist in outpatient care who do NOT provide ovarian cancer screening to asymptomatic, average-risk women (guideline consistent)
Trial locations and contacts
1 site in 1 country.
Germany1 site
Charité - Universitätsmedizin Berlin
Mitte, State of Berlin, 10117
About this study
Official title: 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.
Read the 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:
- 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.
- 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.
- Being tested
- ChatGPT - Control, ChatGPT - Evidence-Based Screening Discussion
- Keywords
- ovarian cancer screening recommendations, AI-based conversational intervention, gynecologists' knowledge on ovarian cancer screening
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ChatGPT - ControlBehavioural
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.
ChatGPT - Evidence-Based Screening DiscussionBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Control (ChatGPT Control Condition) | Other | 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?" Receives: Behavioral: ChatGPT - Control |
| Experimental (ChatGPT Evidence-Based Screening Discussion) | Experimental | 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?" Receives: Behavioral: ChatGPT - Evidence-Based Screening Discussion |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in intention to recommend ovarian cancer screening
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.
Measured: Immediately post intervention
Secondary outcomes (2)
Change in benefit-harm ratio evaluation of ovarian cancer screenings
Difference between the self-reported benefit-harm ratio evaluation before and after the ChatGPT interaction.
Measured: Immediately post intervention
Accuracy of knowledge regarding ovarian cancer screening evidence
Participants' understanding of benefits, harms, and guideline recommendations for ovarian cancer screening, assessed via survey questions
Measured: Immediately post intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2026
- Study start27 Mar 2026
- Primary completion15 Sep 2026
- Study completion15 Sep 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Ovarian Cancer Screening Recommendations by GynecologistsStudies of the same condition, open and under way.
- More trials run by Charite University, Berlin, GermanyThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 300 participants (actual)
Sponsor and investigators
- Lead sponsor
- Charite University, Berlin, Germany
- Sponsor type
- University, hospital or other
- Collaborators
- Max Planck Institute for Human Development, German Research Foundation
- Responsible party
- Principal investigator, Odette Wegwarth, Charite University, Berlin, Germany
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07503054
- Sponsor's protocol ID
- 2025ChatGPTGyn
- Acronym
- AI-OCS-Gyn
- Status last verified
- Oct 2026
- First submitted
- 25 Mar 2026
- Last updated
- 20 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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).
Publications
Wegwarth O, Gigerenzer G. US gynecologists' estimates and beliefs regarding ovarian cancer screening's effectiveness 5 years after release of the PLCO evidence. Sci Rep. 2018 Nov 21;8(1):17181. doi: 10.1038/s41598-018-35585-z.
PubMed 30464251Wegwarth O, Pashayan N. When evidence says no: gynaecologists' reasons for (not) recommending ineffective ovarian cancer screening. BMJ Qual Saf. 2020 Jun;29(6):521-524. doi: 10.1136/bmjqs-2019-009854. Epub 2019 Nov 8. No abstract available.
PubMed 31704891US Preventive Services Task Force; Grossman DC, Curry SJ, Owens DK, Barry MJ, Davidson KW, Doubeni CA, Epling JW Jr, Kemper AR, Krist AH, Kurth AE, Landefeld CS, Mangione CM, Phipps MG, Silverstein M, Simon MA, Tseng CW. Screening for Ovarian Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2018 Feb 13;319(6):588-594. doi: 10.1001/jama.2017.21926.
PubMed 29450531