NCT07503054

Ovarian Cancer Screening and AI

AI on Ovarian Cancer Screening Attitudes in Gynecologists

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 350 (estimated)
• Sponsor: Charite University, Berlin, Germany · Academic / Other
• Sex: All
• Age: 24 Years
• Healthy volunteers: Not accepted

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 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: 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.

Conditions

• Ovarian Cancer Screening Recommendations by Gynecologists

Interventions

Type	Name	Description
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.
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.

Timeline

• Start date: 2026-03-27
• Primary completion: 2026-04-30
• Completion: 2026-04-30
• First posted: 2026-03-31
• Last updated: 2026-03-31

Locations

1 site across 1 country: Germany