IMAS: A Comprehensive Agentic Approach to Rural Healthcare Delivery
Since the onset of COVID-19, rural communities worldwide have faced significant challenges in accessing healthcare due to the migration of experienced medical professionals to urban centers. Semi-trained caregivers, such as Community Health Workers (CHWs) and Registered Medical Practitioners (RMPs), have stepped in to fill this gap, but often lack formal training. This paper proposes an advanced agentic medical assistant system designed to improve healthcare delivery in rural areas by utilizing Large Language Models (LLMs) and agentic approaches. The system is composed of five crucial components: translation, medical complexity assessment, expert network integration, final medical advice generation, and response simplification. Our innovative framework ensures context-sensitive, adaptive, and reliable medical assistance, capable of clinical triaging, diagnostics, and identifying cases requiring specialist intervention. The system is designed to handle cultural nuances and varying literacy levels, providing clear and actionable medical advice in local languages. Evaluation results using the MedQA, PubMedQA, and JAMA datasets demonstrate that this integrated approach significantly enhances the effectiveness of rural healthcare workers, making healthcare more accessible and understandable for underserved populations. All code and supplemental materials associated with the paper and IMAS are available at https://github.com/uheal/imas.
Code (1)
Tasks
MedQASimilar Papers 제목 키워드 기반
Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities that hinder access to essential services. This study investigates the transformative p…
Fine-Tuning DialoGPT on Common Diseases in Rural Nepal for Medical Conversations
Conversational agents are increasingly being explored to support healthcare delivery, particularly in resource-constrained settings such as rural Nepal. Large-scale conversational models typically rely on internet connec…
Domain AdaptationRural Healthcare Access and Supply Constraints: A Causal Analysis
Certificate-of-need (CON) laws require that healthcare providers receive approval from a state board before offering additional services in a given community. Proponents of CON laws claim that these laws are needed to pr…
Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives
Generative Artificial Intelligence (GenAI) is taking the world by storm. It promises transformative opportunities for advancing and disrupting existing practices, including healthcare. From large language models (LLMs) f…
High Altitude Platform-Based Caching and Multicasting for Rural Connectivity
Providing efficient and reliable content delivery in rural areas remains a significant challenge due to the lack of communication infrastructure. To bridge the digital divide, this paper investigates the potential of lev…