Use and Acceptance of Large Language Models for Cancer Shared Decision-Making

NCT07526441 | Completed

• 1 other identifier: LLM-SDM-2025
• Study Type: observational
• Target: 7,151
• Locations: 1 country
• Sites: 1

Brief Summary

This study examines how cancer patients, the general public, and healthcare professionals use and perceive large language models (such as ChatGPT) for health-related shared decision-making in oncology. A cross-sectional survey was conducted among 7,151 participants across 30 countries using a questionnaire developed and validated through a two-round Delphi process involving 44 experts. The study assessed current patterns of large language model use for health information, barriers to adoption including concerns about reliability and privacy, future expectations regarding these tools in shared decision-making, and demographic predictors of adoption. Participants were recruited through the Prolific platform between March and May 2025, with stratified sampling across three groups: cancer patients diagnosed within the past five years, general population members from the United States and United Kingdom, and licensed healthcare professionals with active patient contact.

Conditions

Cancer; Shared Decision Making; Artificial Intelligence

Keywords

Large Language Models; Shared Decision Making; Oncology; Patient Acceptance

Outcome Measures

Primary Outcomes (3)

• Healthcare-specific large language model usage rate

  Proportion of participants reporting use of large language models specifically for health-related information, measured on a 5-point Likert frequency scale and dichotomised as use versus non-use.

  At time of survey completion (single assessment, March-May 2025)

• Future belief in large language model improvement of shared decision-making

  Proportion of participants believing that large language models will improve the quality of shared decision-making in oncology, assessed via Likert-scale response.

  At time of survey completion (single assessment, March-May 2025)

• Barriers to large language model adoption

  Prevalence of concerns regarding large language model use for health decisions, including reliability concerns, privacy concerns, and preference for human interaction, each assessed as binary (present or absent).

  At time of survey completion (single assessment, March-May 2025)

Secondary Outcomes (3)

• Independent predictors of large language model adoption

  At time of survey completion (single assessment, March-May 2025)

• User segmentation

  At time of survey completion (single assessment, March-May 2025)

• Healthcare professional recommendation patterns

  At time of survey completion (single assessment, March-May 2025)

Study Arms (3)

Cancer Patients

Adults aged 18 years or older with a self-reported cancer diagnosis within the past five years, recruited through the Prolific platform with verification through screening questions about diagnosis date, cancer type, and treatment status. n=2,316.

General Population

Adults aged 18 years or older from the United States and United Kingdom with no specific health condition requirement, recruited through the Prolific platform with stratified sampling quotas for age, gender, ethnicity, and education. n=2,000.

Healthcare Professionals

Licensed healthcare practitioners aged 18 years or older with active patient contact, including physicians and nursing staff, recruited through the Prolific platform from the United States, United Kingdom, and 28 additional countries. n=2,835.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Three cohorts recruited via the Prolific platform: cancer patients with a diagnosis within five years (n=2,316), general population members from the United States and United Kingdom (n=2,000), and licensed healthcare professionals with active patient contact (n=2,835). Stratified sampling applied quotas for age, gender, ethnicity, and education in the general population cohort.

You may qualify if:

• Age 18 years or older
• English language proficiency
• Regular internet access
• Registered on the Prolific research platform
• For cancer patient cohort: self-reported cancer diagnosis within the past five years
• For healthcare professional cohort: licensed healthcare practitioner with active patient contact
• For general population cohort: resident of the United States or United Kingdom

You may not qualify if:

• Failure on embedded attention check questions (4 checks)
• Survey completion time less than 5 minutes or greater than 60 minutes
• Straight-line responding pattern detected by consistency validation algorithms
• Failure of cohort verification procedures

Sponsors & Collaborators

• Technical University of Munich: lead

Study Sites (1)

Technical University Munich

Munich, Bavaria, 81675, Germany

Location

MeSH Terms

Conditions

Neoplasms

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 6, 2026
• First Posted: April 13, 2026
• Study Start: March 1, 2025
• Primary Completion: May 1, 2025
• Study Completion: May 1, 2025
• Last Updated: April 30, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)