Moaiz Abrar | Searcharxiv

arXiv2025

System X: A Mobile Voice-Based AI System for EMR Generation and Clinical Decision Support in Low-Resource Maternal Healthcare

We present the design, implementation, and in-situ deployment of a smartphone-based voice-enabled AI system for generating electronic medical records (EMRs) and clinical risk alerts in maternal healthcare settings. Targeted at low-resource environments such as Pakistan, the system integrates a fine-tuned, multilingual automatic speech recognition (ASR) model and a prompt-engineered large language model (LLM) to enable healthcare workers to engage naturally in Urdu, their native language, regardless of literacy or technical background. Through speech-based input and localized understanding, the system generates structured EMRs and flags critical maternal health risks. Over a seven-month deployment in a not-for-profit hospital, the system supported the creation of over 500 EMRs and flagged over 300 potential clinical risks. We evaluate the system's performance across speech recognition accuracy, EMR field-level correctness, and clinical relevance of AI-generated red flags. Our results demonstrate that speech based AI interfaces, can be effectively adapted to real-world healthcare settings, especially in low-resource settings, when combined with structured input design, contextual medical dictionaries, and clinician-in-the-loop feedback loops. We discuss generalizable design principles for deploying voice-based mobile healthcare AI support systems in linguistically and infrastructurally constrained settings.

cs.HC↗

arXiv2024

An Empirical Evaluation of Large Language Models on Consumer Health Questions

This study evaluates the performance of several Large Language Models (LLMs) on MedRedQA, a dataset of consumer-based medical questions and answers by verified experts extracted from the AskDocs subreddit. While LLMs have shown proficiency in clinical question answering (QA) benchmarks, their effectiveness on real-world, consumer-based, medical questions remains less understood. MedRedQA presents unique challenges, such as informal language and the need for precise responses suited to non-specialist queries. To assess model performance, responses were generated using five LLMs: GPT-4o mini, Llama 3.1: 70B, Mistral-123B, Mistral-7B, and Gemini-Flash. A cross-evaluation method was used, where each model evaluated its responses as well as those of others to minimize bias. The results indicated that GPT-4o mini achieved the highest alignment with expert responses according to four out of the five models' judges, while Mistral-7B scored lowest according to three out of five models' judges. This study highlights the potential and limitations of current LLMs for consumer health medical question answering, indicating avenues for further development.

cs.CL↗