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
- Are there results?
- None posted in the registry
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
Study at a glance
From the registry record, last updated by the sponsor on 20 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI on Ovarian Cancer Screening Attitudes in Gynecologists
- ClinicalTrials.gov ID
- NCT07503054
- Acronym / sponsor ID
- AI-OCS-Gyn / 2025ChatGPTGyn
- Keywords
- ovarian cancer screening recommendations, AI-based conversational intervention, gynecologists' knowledge on ovarian cancer screening
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 300 participants (actual)
- Lead sponsor
- Charite University, Berlin, GermanyUniversity, hospital or other
- Collaborators
- Max Planck Institute for Human Development, German Research Foundation
- Study start
- 27 Mar 2026 (actual)
- Primary completion
- 15 Sep 2026 (actual)
- Study completion
- 15 Sep 2026 (actual)
- First posted
- 31 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control (ChatGPT Control Condition) | Other | Behavioral: ChatGPT - ControlParticipants 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?" |
| Experimental (ChatGPT Evidence-Based Screening Discussion) | Experimental | Behavioral: ChatGPT - Evidence-Based Screening DiscussionParticipants 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?" |
Who may qualify
- Ages
- 24 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- gynecologists in outpatient care who provide ovarian cancer screening to asymptomatic, average-risk women (not guideline consistent)
Key exclusion criteria (2 listed)
- gynecologists in inpatient care
- gynecologist in outpatient care who do NOT provide ovarian cancer screening to asymptomatic, average-risk women (guideline consistent)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ChatGPT - ControlThree-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. | Behavioural | Control (ChatGPT Control Condition) |
| ChatGPT - Evidence-Based Screening DiscussionThree-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. | Behavioural | Experimental (ChatGPT Evidence-Based Screening Discussion) |
Primary outcome
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.
2 secondary outcomes
Change in benefit-harm ratio evaluation of ovarian cancer screenings
Time frame: Immediately post intervention
Accuracy of knowledge regarding ovarian cancer screening evidence
Time frame: Immediately post intervention
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Oct 2026.
- Background: PubMed 30464251Wegwarth 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.
- Background: PubMed 31704891Wegwarth 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.
- Background: PubMed 29450531US 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.
- Individual participant dataSharing planned: 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).
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
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.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in intention to recommend ovarian cancer screening (time frame: Immediately post intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07503054, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Oct 2026.