Ovarian Cancer Screening and AI

Официальное название

AI on Ovarian Cancer Screening Attitudes in Gynecologists

Обзор

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.

Подробное описание

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.

Вмешательства

• Поведенческое 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.
• Поведенческое 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.

Первичные конечные точки

• Change in intention to recommend ovarian cancer screening [Срок оценки: Immediately post intervention]

Вторичные конечные точки (2)

• Change in benefit-harm ratio evaluation of ovarian cancer screenings [Срок оценки: Immediately post intervention]
• Accuracy of knowledge regarding ovarian cancer screening evidence [Срок оценки: Immediately post intervention]

Критерии участия

Критерии включения

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

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Простое слепое

Основная цель

Скрининг

Центры проведения

Германия · 1 центр

• Charité - Universitätsmedizin Berlin — Mitte

Публикации

• 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. PMID 30464251
• Wegwarth 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. PMID 31704891
• US 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. PMID 29450531

Идентификаторы

NCT: NCT07503054 · 2025ChatGPTGyn

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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