The Influence of Patient Use of Artificial Intelligence on Doctor-Patient Interaction and Clinical Outcomes in Endometriosis Consultations

NCT07393568 | Not Yet Recruiting

• 1 other identifier: 0423-25-HMO
• Study Type: observational
• Target: 94
• Locations: 0 countries
• Sites: N/A

Brief Summary

Generative artificial intelligence (AI), including large language models such as ChatGPT, Gemini, and Copilot, is increasingly used by patients to obtain medical information and prepare for clinical encounters. Although these tools often provide guideline-consistent information, their responses may be incomplete, inaccurate, or lack personalization, potentially influencing patient expectations and clinical interactions. The impact of patient AI use on satisfaction, adherence, and physician-patient communication remains poorly understood. This prospective comparative study will evaluate the effects of patient AI use prior to gynecologic consultations for endometriosis. Women attending a specialized endometriosis clinic will be categorized as AI users or non-users based on their preparation for the visit. Patient-reported outcomes, including satisfaction, expectations, adherence to physician recommendations, and pain during physical examination, will be assessed using validated questionnaires and visual analogue scales. Physicians, blinded to AI use, will independently assess patient engagement, trust, and compliance. Visit duration will also be recorded. The primary objective is to determine whether AI use affects patient satisfaction and adherence to treatment recommendations. Secondary objectives include evaluating physician-perceived interaction quality and concordance between AI-generated guidance and physician recommendations. Findings from this study will provide critical evidence on how AI influences patient behavior and clinical care in endometriosis, informing best practices for integrating AI-informed patients into routine clinical encounters.

Conditions

Endometriosis

Outcome Measures

Primary Outcomes (1)

• Patient Satisfaction and Treatment Plan Adherence After Consultation

  Overall patient satisfaction will be measured immediately after the endometriosis consultation using a structured post-visit questionnaire and reported as a score on a 0-5 scale, where 0 indicates not satisfied at all and 5 indicates very satisfied. Intention to adhere to the physician's treatment plan will be measured immediately after the consultation as a binary outcome (yes/no) based on the patient's response to the post-visit questionnaire item asking whether she intends to follow the doctor's treatment recommendations.

  Immediately after the consultation (same day)

Secondary Outcomes (1)

• Physician-Perceived Interaction Quality and Consultation Characteristics

  During and immediately after the consultation (same day)

Study Arms (2)

using Chat gpt before outpatient clinic visit

using Chat gpt before outpatient clinic visit

Other: using Chat gpt before outpatient clinic visit

Chat -gpt non users

Interventions

using Chat gpt before outpatient clinic visit OTHER

This study involves a behavioral, non-randomized observational intervention based on patients' self-directed use of generative artificial intelligence (AI) tools prior to their clinical visit. The intervention group consists of patients who report using AI-based large language models (e.g., ChatGPT or similar tools) to prepare for their endometriosis-related consultation. AI use may include seeking information about symptoms, diagnosis, treatment options, prognosis, or formulating questions for the physician. No specific AI platform, prompts, or duration of use is mandated, and AI engagement occurs independently and outside the clinical setting. The control group includes patients who report no use of AI tools in preparation for the visit. No AI tools are introduced, recommended, or used during the clinical encounter by study personnel. Physicians are blinded to patient AI use status and conduct consultations according to standard clinical practice. Aside from questionnaire administ

using Chat gpt before outpatient clinic visit

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: endometriosis is only womens disease
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of adult women (aged ≥18 years) attending a specialized gynecology outpatient clinic for evaluation or management of endometriosis-related complaints. Eligible participants must be able to provide informed consent and complete study questionnaires independently. Participants will include both new and returning patients with suspected or confirmed endometriosis. Individuals with cognitive impairment or psychiatric conditions that significantly interfere with communication or the ability to provide informed consent will be excluded. Following enrollment, participants will be categorized based on self-reported use of generative artificial intelligence (AI) tools to prepare for their clinical visit. Demographic and baseline clinical characteristics-including age, ethnicity, body mass index, smoking status, medical comorbidities, reproductive history, and prior surgical history-will be collected to characterize the study population and allow for compariso

You may qualify if:

• Women aged ≥18.
• Attending clinic for endometriosis-related complaints.
• Able to give informed consent.

You may not qualify if:

• Cognitive impairment or psychiatric conditions that affect communication or the ability to provide informed consent

Sponsors & Collaborators

• Hadassah Medical Organization: lead

MeSH Terms

Conditions

Endometriosis

Condition Hierarchy (Ancestors)

Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 17, 2025
• First Posted: February 6, 2026
• Study Start: February 1, 2026
• Primary Completion (Estimated): January 1, 2027
• Study Completion (Estimated): January 1, 2027
• Last Updated: February 6, 2026

Record last verified: 2026-01