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

May 14, 2026 updated by: Ayşe Gül Bursa, Fenerbahce University

Digital Transformation in Women's Health: The Impact of AI-Powered Training on Gynecological Examination Anxiety and Satisfaction

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

114

Phase

  • Not Applicable

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • Applying to the outpatient clinic for a gynecological examination
  • Being between 18-65 years of age
  • Agreeing to participate in the study

Exclusion Criteria:

  • Communication barrier
  • Having a psychological diagnosis,
  • Being pregnant

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Supportive Care
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: intervention group
Groups that will receive AI-assisted training before gynecological examinations.
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.
No Intervention: control group
group that will not be intervened with

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Women's anxiety levels
Time Frame: through study completion, an average of 1 hour.
This study aims to investigate the effect of an AI-assisted training intervention on women's anxiety levels during gynecological examinations. The Gynecological Examination Anxiety Scale will be administered before and after the gynecological examination. The scale consists of 20 items and is divided into 5 sub-dimensions: healthcare personnel approach, healthcare personnel experience, negative experiences, hygienic reasons, and individual attitudes. The Cronbach's Alpha coefficient of the scale was determined to be 0.867 (Demirtop, 2014).While there are no items that are reverse-scored on the scale, a high score indicates high anxiety.
through study completion, an average of 1 hour.
Women's satisfaction level
Time Frame: through study completion, an average of 1 hour.
The Outpatient Patient Satisfaction Scale, developed by Kevenk, Kantas-Yilmaz, and Ozturk, consists of 26 items and 4 dimensions: examination, diagnosis and treatment process, physical environment, appointment process, and communication. The Cronbach's Alpha coefficient of the scale was determined to be 0.947 (Kevenk, Kantas-Yilmaz, and Ozturk, 2021). An increase in the score obtained from the scale indicates increased satisfaction.
through study completion, an average of 1 hour.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fenerbahce University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 2, 2026

Primary Completion (Estimated)

August 30, 2026

Study Completion (Estimated)

September 29, 2026

Study Registration Dates

First Submitted

April 29, 2026

First Submitted That Met QC Criteria

May 14, 2026

First Posted (Actual)

May 20, 2026

Study Record Updates

Last Update Posted (Actual)

May 20, 2026

Last Update Submitted That Met QC Criteria

May 14, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Fenerbahce Uni

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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