Clinical trial · Observational
Use and Acceptance of Large Language Models for Cancer Shared Decision-Making
Use and Acceptance of Large Language Models in Oncological Shared Decision-Making Among Patients, the Public, and Healthcare Professionals
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence | — | UNRESOLVED | — |
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
| Shared Decision Making | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Cancer Patients
- description
- 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.
- label
- General Population
- description
- 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.
- label
- Healthcare Professionals
- description
- 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.
Primary outcomes (3)
- measure
- Healthcare-specific large language model usage rate
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * 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
References
Publications (0)
Data not yet available