Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis

Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e65567 - e65567

Опубликована: Март 21, 2025

Background Artificial intelligence (AI) has potential to transform health care, but its successful implementation depends on the trust and acceptance of consumers patients. Understanding factors that influence attitudes toward AI is crucial for effective adoption. Despite AI’s growing integration into consumer patient remains a critical challenge. Research largely focused applications or attitudes, lacking comprehensive analysis how factors, such as demographics, personality traits, technology knowledge, affect interact across different care contexts. Objective We aimed investigate people’s in use cases determine context perceived risk individuals’ propensity accept specific scenarios. Methods collected analyzed web-based survey data from 1100 Finnish participants, presenting them with 8 care: 5 (62%) noninvasive (eg, activity monitoring mental support) 3 (38%) physical interventions AI-controlled robotic surgery). Respondents evaluated intention use, trust, willingness trade off personal these cases. Gradient boosted tree regression models were trained predict responses based 33 demographic-, personality-, technology-related variables. To interpret results our predictive models, we used Shapley additive explanations method, game theory–based approach explaining output machine learning models. It quantifies contribution each feature individual predictions, allowing us relative importance various their interactions shaping participants’ care. Results Consumer technology, traits primary drivers Use ranked by acceptance, monitors being most preferred. However, case had less impact general than expected. Nonlinear dependencies observed, including an inverted U-shaped pattern positivity self-reported knowledge. Certain more disorganized careless, associated positive Women seemed cautious about men. Conclusions The findings highlight complex interplay influencing are driven rather service providers should consider demographic when designing implementing systems study demonstrates using decision-making tools interacting clients applications.

Язык: Английский

Multiclass Classification of Mental Health Disorders Using XGBoost-HOA Algorithm

SN Computer Science, Год журнала: 2024, Номер 5(8)

Опубликована: Дек. 12, 2024

Язык: Английский

Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis (Preprint)

Опубликована: Авг. 20, 2024

Artificial intelligence (AI) has potential to transform health care, but its successful implementation depends on the trust and acceptance of consumers patients. Understanding factors that influence attitudes toward AI is crucial for effective adoption. Despite AI’s growing integration into consumer patient remains a critical challenge. Research largely focused applications or attitudes, lacking comprehensive analysis how factors, such as demographics, personality traits, technology knowledge, affect interact across different care contexts. We aimed investigate people’s in use cases determine context perceived risk individuals’ propensity accept specific scenarios. collected analyzed web-based survey data from 1100 Finnish participants, presenting them with 8 care: 5 (62%) noninvasive (eg, activity monitoring mental support) 3 (38%) physical interventions AI-controlled robotic surgery). Respondents evaluated intention use, trust, willingness trade off personal these cases. Gradient boosted tree regression models were trained predict responses based 33 demographic-, personality-, technology-related variables. To interpret results our predictive models, we used Shapley additive explanations method, game theory–based approach explaining output machine learning models. It quantifies contribution each feature individual predictions, allowing us relative importance various their interactions shaping participants’ care. Consumer technology, traits primary drivers Use ranked by acceptance, monitors being most preferred. However, case had less impact general than expected. Nonlinear dependencies observed, including an inverted <i>U</i>-shaped pattern positivity self-reported knowledge. Certain more disorganized careless, associated positive Women seemed cautious about men. The findings highlight complex interplay influencing are driven rather service providers should consider demographic when designing implementing systems study demonstrates using decision-making tools interacting clients applications.

Язык: Английский