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The HEART Framework for LLM-Enabled Socially Assistive Robots in Healthcare: A PRISMA-Informed Structured Review
- Truvace record
- TRV-2026-2564raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 1aa008a377ae…check it
Large language models (LLMs) are expanding the capabilities of socially assistive robots (SARs) through natural dialogue, personalisation, multimodal reasoning, retained interaction context, and adaptive behaviour in healthcare. Integrating generative language models into robots, however, complicates evaluation because fluent output may exaggerate perceived competence and increase the risks of hallucination, overtrust, privacy exposure, relationship dependency, and unsafe reliance on advice or actions. This PRIS…
The problem
A structured review of 85 sources found studies of LLM-enabled healthcare robots focused on feasibility, usability and short-term engagement, while longitudinal safety, governance of retained interaction context and sustained healthcare value received limited attention.
What this doesn’t fix
The HEART framework is a proposal that has not yet been validated; the authors say future research should test it through longitudinal and comparative assessment.
Sources
1 on file- Applied Sciencesdoi:10.3390/app16167904
Cite this record
v1Truvace Impact Record TRV-2026-2564, v1: “The HEART Framework for LLM-Enabled Socially Assistive Robots in Healthcare: A PRISMA-Informed Structured Review.” Truvace, 2026-11-12. /record/TRV-2026-2564 (accessed on the day you copy it). sha256 1aa008a377ae272e…
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