Clinical AI Applications in the Context of Cancer: Explaining Openness to Information and Consent to Use Through Risk Perceptions and Efficacy Beliefs

The use of artificial intelligence (AI) in oncology holds immense potential for improving diagnostic accuracy and treatment outcomes. Yet, concerns about potential risks need to be addressed. To improve acceptance, understanding, and trust, it is crucial to provide individuals with accurate information about AI in medicine. Building on the Risk Perception Attitude Framework (RPA), this study examines how combinations of AI-related risk perceptions and AI-information efficacy beliefs shape individuals' openness to information and consent to the use of AI-assisted cancer care. Data from an online survey of the German population stratified by age, gender, and education (<i>n</i> = 3,274) were segmented into four groups based on different patterns of AI-related risk perceptions and AI-information efficacy beliefs using the RPA framework. Results show significant differences in information-seeking intentions and willingness to consent to the use of AI in oncology care across groups. Our study extends the RPA framework to technological innovations in medicine and offers implications for targeted strategies for health communication campaigns aimed at improving acceptance of AI use in oncology.

Authors

Institutions

Publication Details

Journal
Journal of Health Communication
Published
2026-02-25
DOI
https://doi.org/10.1080/10810730.2026.2636816
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical AI Applications in the Context of Cancer: Explaining Openness to Information and Consent to Use Through Risk Perceptions and Efficacy Beliefs

Tanja Fisse, Magdalena Rosset, Charlotte Schrimpff, Eva Baumann
Journal of Health Communication
Artificial Intelligence in Healthcare and Education
article

Clinical AI Applications in the Context of Cancer: Explaining Openness to Information and Consent to Use Through Risk Perceptions and Efficacy Beliefs

Tanja Fisse, Magdalena Rosset, Charlotte Schrimpff, Eva Baumann
article en

Abstract

The use of artificial intelligence (AI) in oncology holds immense potential for improving diagnostic accuracy and treatment outcomes. Yet, concerns about potential risks need to be addressed. To improve acceptance, understanding, and trust, it is crucial to provide individuals with accurate information about AI in medicine. Building on the Risk Perception Attitude Framework (RPA), this study examines how combinations of AI-related risk perceptions and AI-information efficacy beliefs shape individuals' openness to information and consent to the use of AI-assisted cancer care. Data from an online survey of the German population stratified by age, gender, and education (<i>n</i> = 3,274) were segmented into four groups based on different patterns of AI-related risk perceptions and AI-information efficacy beliefs using the RPA framework. Results show significant differences in information-seeking intentions and willingness to consent to the use of AI in oncology care across groups. Our study extends the RPA framework to technological innovations in medicine and offers implications for targeted strategies for health communication campaigns aimed at improving acceptance of AI use in oncology.

Journal of Health Communication
Hanover University of Music Drama and Media (DE)
Quality Education
Openalex Percentile: Top 42%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.