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TRUVACE RECORD VERSION
record: TRV-2026-2564
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:54:59.569267Z
status: published
lens: p_space
sector: health
headline: The HEART Framework for LLM-Enabled Socially Assistive Robots in Healthcare: A PRISMA-Informed Structured Review
dek: 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…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: Studies focused mainly on feasibility, usability, acceptability, dialogue quality, and short-term engagement | longitudinal safety, governance of retained interaction context, comparative effectiveness, workflow integration, and sustained healthcare value received limited attention
quick_read: A PRISMA-informed review of research on socially assistive robots powered by large language models in healthcare found that the evidence base leans heavily toward early-stage questions. Database searches returned 128 records, 110 of them unique, and supplementary retrieval left 85 substantive sources for analysis.

Those studies mostly measured feasibility, usability, acceptability, dialogue quality and short-term engagement. Longitudinal safety, comparative effectiveness, workflow integration and sustained healthcare value got little attention, according to the review.

The authors argue that fluent output from language models may exaggerate how competent a robot seems, which raises risks of hallucination, overtrust, privacy exposure, relationship dependency and unsafe reliance on advice or actions. They propose a framework called HEART and call for longitudinal and comparative validation.
limitation: 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.
tag: Evidence-backed problem
key_points: Database searches returned 128 records, of which 110 were unique after deduplication. | Supplementary retrieval and assessment yielded 85 substantive sources covering background mapping, primary analysis and governance. | Most studies examined feasibility, usability, acceptability, dialogue quality and short-term engagement. | Longitudinal safety, governance of retained interaction context, comparative effectiveness and workflow integration received limited attention. | The authors say evaluation must account for physical presence, social role, interaction memory and potential actions, not conversational performance alone. | HEART has five domains: Human-Centred Communication, Ethical and Trustworthy Deployment, Adaptive and Embodied Intelligence, Relationship Continuity, and Translational Healthcare Value. | The framework uses non-additive deployment gates so that strengths in one area cannot mask critical safety or governance failures.
rundown: The review covers healthcare robotics, human-robot interaction, LLM-enabled systems, ethics, implementation and care delivery. Language models are adding natural dialogue, personalisation, multimodal reasoning, retained interaction context and adaptive behaviour to socially assistive robots.

The authors say these gains complicate evaluation. Fluent output may make a robot seem more competent than it is, and a robot with physical presence, a social role and memory of past interactions carries risks beyond those of a text chatbot.

HEART uses boundary rules, operational indicators, qualitative labels and non-additive deployment gates to separate evaluative domains, define assessable outcomes and summarise reported support.

The authors recommend validating HEART through longitudinal and comparative assessment of hallucination severity, language-to-action safety, long-term effects, equity and post-deployment monitoring.
sources:
- peer_reviewed | Applied Sciences | https://doi.org/10.3390/app16167904 | 2026-08-07
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