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

Shared decision-making is a collaborative process in which clinicians support patients in reaching treatment decisions. Despite its importance in oncology, structured shared decision-making remains uncommon in routine clinical practice. Large language models offer a new way for patients to access and understand medical information, yet little is known about how key stakeholders perceive and use these tools for health decisions.

This observational study used a sequential mixed-methods design combining Delphi consensus methodology with cross-sectional survey deployment. A 44-expert panel across eight domains (clinical artificial intelligence, technical development, oncology, psychology, epidemiology, patient advocacy, ethics, and legal expertise) developed and validated the assessment instrument through two Delphi rounds, achieving consensus on 89 items. The final instrument contained 52 quantitative items and 8 qualitative prompts, distinguishing between general and healthcare-specific large language model use.

The study recruited three cohorts: 2,316 cancer patients with self-reported diagnosis within five years, 2,000 general population members from the United States and United Kingdom, and 2,835 licensed healthcare professionals. Quality control included attention checks, completion time monitoring, consistency validation, and verification procedures, resulting in exclusion of 694 responses (8.8%) from an initial 7,845.

Primary analyses included chi-squared testing and ANOVA with Bonferroni correction, multivariable logistic regression with hierarchical model building to identify adoption predictors, and user segmentation through cross-tabulation combined with k-means clustering. The study was approved by the institutional review board of the Technical University of Munich (TUM2024-89-S-SB).

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