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The Impact of AI-Powered Training on Gynecological Examination Anxiety and Satisfaction

NCT07599358 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 114

Last updated 2026-05-20

No results posted yet for this study

Summary

Gynecological cancers, particularly cervical, ovarian, and endometrial cancers, pose a global problem. Cervical cancers are quite common worldwide, and this rate is even higher in developing countries. Cervical cancers are easily treatable when detected early, and screening is quite easy. Diagnosis is routinely made through human papillomavirus (HPV) testing and cytological screening. Eliminating anxiety, fear, and uncertainty about gynecological examinations makes the examination process easier, thus enabling early diagnosis and treatment of diseases. Keeping up with developing and changing technology and using it to improve women's health is an undeniable change in recent times. This study aims to determine the effect of an AI-assisted informational training program on women's anxiety and satisfaction levels regarding gynecological examinations.

Conditions

  • Gynecological Examination
  • Artificial Intelligence (AI)
  • Patient Satisfaction
  • Anxiety

Interventions

BEHAVIORAL

AI-assisted education

As an initiative, ChatGPT, one of the most commonly used artificial intelligence tools, was asked to prepare a text to provide women with detailed information before gynecological examinations. This text was evaluated by three gynecologists specializing in the field, and necessary adjustments were made. Based on this text, ChatGPT was asked to generate visuals for the relevant text. Using these visuals, a 4.13-minute video was created via Canva to inform patients before their gynecological examinations. Subtitles were added to the video, considering the potential noise level. Women randomly assigned to the intervention group will be shown the video before their examinations.

Sponsors & Collaborators

  • Fenerbahce University

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-05-02
Primary Completion
2026-08-30
Completion
2026-09-29

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07599358 on ClinicalTrials.gov