Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use
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Abstract (arXiv)
AI assistants such as Alexa, Google Assistant, and Siri, are making their way into the healthcare sector, offering a convenient way for users to access different healthcare services. Trust is a vital factor in the uptake of healthcare services, but the factors affecting trust in voice assistants used for healthcare are under-explored and this specialist domain introduces additional requirements. This study explores the effects of different functional, personal, and risk factors on trust in and adoption of healthcare voice AI assistants (HVAs), generating a partial least squares structural model from a survey of 300 voice assistant users. Our results indicate that trust in HVAs can be significantly explained by functional factors (usefulness, content credibility, quality of service relative to a healthcare professional), together with security, and privacy risks and personal stance in technology. We also discuss differences in terms of trust between HVAs and general-purpose voice assistants as well as implications that are unique to HVAs.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2024.
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Citing papers per year: 2025: 3; 2026: 3.
6 papers.
- 2025Malicious LLM-Based Conversational AI Makes Users Reveal Personal Informationcited by 10
- 2025Voice-based AI Agents: Filling the Economic Gaps in Digital Health Deliverycited by 0
- 2025LingVarBench: Benchmarking LLMs on Entity Recognitions and Linguistic Verbalization Patterns in Phone-Call Transcriptscited by 0
- 2026Generative Confidants: How do People Experience Trust in Emotional Support from Generative AI?cited by 0
- 2026After Talking with 1,000 Personas: Learning Preference-Aligned Proactive Assistants From Large-Scale Persona Interactionscited by 0
- 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systemscited by 0
- 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems24 Feb 2026
- 2026After Talking with 1,000 Personas: Learning Preference-Aligned Proactive Assistants From Large-Scale Persona Interactions3 Feb 2026
- 2026Generative Confidants: How do People Experience Trust in Emotional Support from Generative AI?23 Jan 2026
- 2025LingVarBench: Benchmarking LLMs on Entity Recognitions and Linguistic Verbalization Patterns in Phone-Call Transcripts13 Aug 2025
- 2025Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery22 Jul 2025
- 2025Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information13 Jun 2025