ArticleStudies in health technology and informatics2025
Acceptability of Academic Large Language Model for Patients Seeking Health Information.
Article in Studies in health technology and informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Usability and User Experience Assessment of Health Care Conversational Agents Using Validated Subjective Instruments: Systematic Review and Comparative Analysis.JMIR human factors · 2026Pooled it
- Trust-Centered Design and Feasibility Evaluation of an AI-Enabled Conversational Health Tool for Sexual and Reproductive Health Among Rural Young Adults: Protocol for a Mixed Methods Study.JMIR research protocols · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The use of large language models (LLMs) in healthcare poses challenges related to data security, accuracy, bias, and usability, which can hinder their effectiveness in enhancing patients ability to locate trusted health information. We conducted a pilot study of a local LLM named "SAM," built upon the Llama 7B architecture. Participants engaged with SAM by submitting health-related questions across five health domains. Following their interactions, participants completed the System Usability Scale (SUS) to evaluate the usability of the model. Of the ten participants, eight (80%) were female, and two (20%) were male. The highest-rated theme was ease of learning, with participants strongly agreeing that most people would quickly learn to use the chatbot (Mean = 4.7, SD = 0.46). Developing a local LLM for patient health information must tackle healthcare barriers. By enhancing data security, personalizing responses, and increasing user familiarity, SAM can improve patient engagement and outcomes.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.