IntroductionIn healthcare, socially assistive robots are increasingly used for logistical, assistive, and psychosocial purposes, raising ethical, social, and organizational questions. In these contexts, professionals’ acceptability varies by use case, perceived risk, and care setting. Understanding how healthcare professionals evaluate these technologies is essential for anticipating their large-scale integration into health systems and its implications for workforce organization and equity of access.MethodsThis cross-sectional survey in France examined healthcare professionals’ perceptions of socially assistive robots, focusing on perceived usefulness, acceptability, and implementation-related factors. A self-administered 48-item questionnaire covered sociodemographic characteristics, knowledge of robots, perceived usefulness across use cases, importance of implementation factors, and acceptability and intention to use. Data were analyzed using descriptive statistics, non-parametric tests, and principal component analysis with hierarchical clustering to identify attitudinal profiles.ResultsA total of 148 healthcare professionals participated, 77% reporting prior knowledge of robots. Perceived usefulness was generally high, particularly for physical tasks and recreational support, while therapeutic mediation and feeding were rated lower. Ethical, organizational, and regulatory factors were rated as very important, and acceptability was higher for general use than for personal clinical practice. Cluster analysis identified three attitudinal profiles characterized by low, moderate, and high acceptability.DiscussionHealthcare professionals expressed generally favorable but selective attitudes toward socially assistive robots, mainly valuing logistical and organizational support and remaining more cautious about therapeutic and psychosocial uses. Acceptability appeared conditional and context-dependent, linked to perceived usefulness, safeguards, and prior knowledge rather than professional or sociodemographic characteristics. These findings highlight the need for public-health and implementation strategies combining clear ethical and legal frameworks, training, and context-specific integration, and the relevance of longitudinal mixed-method studies to examine how attitudes and adoption evolve with real-world use.
Performance of large language models in delivering accurate and comprehensible patient information on heart failure and cardiomyopathy
BackgroundLarge language models (LLMs) are increasingly used by patients seeking cardiovascular health information through digital platforms. However, their accuracy and suitability for providing guidance on