Lightweight Large Language Model for Medication Enquiry: Med-Pal

TL;DR

This paper presents Med-Pal, a lightweight, fine-tuned LLM chatbot designed for medication inquiries, demonstrating improved safety, accuracy, and patient communication compared to existing models, suitable for digital health applications.

Contribution

The study introduces Med-Pal, a novel lightweight LLM chatbot fine-tuned for medication questions, with clinical validation showing superior safety and communication quality.

Findings

Mistral-7b achieved highest median score of 14 and 71.9% high-quality responses.

Med-Pal outperformed Biomistral in patient communication appropriateness.

Med-Pal showed reduced bias and errors compared to general LLMs.

Abstract

Large Language Models (LLMs) have emerged as a potential solution to assist digital health development with patient education, commonly medication-related enquires. We trained and validated Med-Pal, a medication domain-specific LLM-chatbot fine-tuned with a fine-grained and expert curated dataset from a selection of five light-weighted open-source LLMs of smaller parameter size (7 billion or less) regarding computational constraints and prioritizing operational efficiency. A multi-disciplinary team performed a clinical evaluation of LLMs responses using the SCORE criteria, focusing on safety, accuracy, bias, reproducibility, and ease of understanding. Best performing light-weighted LLM was chosen as Med-Pal for further engineering with guard-railing using adversarial prompting. Med-Pal and existing light-weighted LLMs, including pretrained Biomistral and finetuned Meerkat, were validated on an independent dataset on a broad range of medication-related questions (231 in total), 12 different question types across 14 different medication classes. Mistral-7b emerged as the top performer among selected lightweight LLMs, achieving the highest median score of 14 and 71.9% high-quality responses in accuracy and safety domains, hence chosen as the backbone LLM for Med-Pal. When compared against Biomistral, Med-pal outperformed in generating responses appropriate for patient communication, with significant reductions bias and errors typical of general LLMs. Comparable performance was observed when comparing Med-Pal with Meerkat. Med-Pal showcases the feasibility of developing and employing fine-tuned light-weighted LLMs to enhance digital health communications.