ArticlePLOS digital health2025
Development and evaluation of a lightweight large language model chatbot for medication enquiry.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Real-World Analysis of Organ Transplantation-Specific Agent Based on Large Language Model in Post-Transplant Self-Management During Off-Hours: A Mixed-Methods Study.Current medical science · 2026Article
- A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.Cell reports. Medicine · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- Performance Modeling of Lightweight Retrieval-Augmented Large Language Models for Low-Resource Plastic Surgery Settings.Bioengineering (Basel, Switzerland) · 2026Article
- Overview of RAG-based and LLM-based approaches to personalization in healthcare AI applications.Frontiers in artificial intelligence · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs) show promise in augmenting digital health applications. However, development and scaling of large models face computational constraints, data security concerns and limitations of internet accessibility in some regions. We developed and tested Med-Pal, a medical domain-specific LLM-chatbot fine-tuned with a fine-grained, expert curated medication-enquiry dataset consisting of 1,100 question and answer pairs. We trained and validated five light-weight, open-source LLMs of smaller parameter size (7 billion or less) on a validation dataset of 231 medication-related enquiries. We introduce SCORE, an LLM-specific evaluation criteria for clinical adjudication of LLM responses, performed by a multidisciplinary expert team. The best performing lighted-weight LLM was chosen as Med-Pal for further engineering with guard-railing against adversarial prompts. Med-Pal outperformed Biomistral and Meerkat, achieving 71.9% high-quality responses in a separate testing dataset. Med-Pal's light-weight architecture, clinical alignment and safety guardrails enable implementation under varied settings, including those with limited digital infrastructure.
Identifiers
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.