Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian

Domaine:

healthcarenatural language processing

Type de record:

papermodelsoftware

Créateur:

NicCosDumRǎd

Hôte:

Text-based telemedicine has become increasingly common, yet the quality of medical advice in doctor-patient interactions is often judged more on how advice is communicated rather than its clinical accuracy. To address this, we introduce Dr. Copilot , a multi-agent large language model (LLM) system that supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses. Rather than assessing medical correctness, Dr. Copilot provides feedback along 17 interpretable axes. The system comprises of three LLM agents with prompts automatically optimized via DSPy. Designed with low-resource Romanian data and deployed using open-weight models, it delivers real-time specific feedback to doctors within a telemedicine platform. Empirical evaluations and live deployment with 41 doctors show measurable improvements in user reviews and response quality, marking one of the first real-world deployments of LLMs in Romanian medical settings. 10 figures, 2 tables, 2 listings

Visit

arxiv.org

Similaires

Universally Expressive Communication in Multi-Agent Reinforcement LearningEmergent Communication in Multi-Agent Reinforcement Learning for Flying Base StationsSociolinguistic Study of Gender and Health Literacy in Doctor–Patient Communication in Ilorin HospitalsMulti-Agent Systems Requirements Analysis for Patient-centered Healthcare Consultancy ServiceNecroraijin/AEGIS-AI-Doctor-Assistant-AppENHANCING HEALTHCARE COMMUNICATION: A YORUBA-TO-ENGLISH NLP MODEL FOR DOCTOR-PATIENT INTERACTIONS USING MACHINE TRANSLATION AND SPEECH-TO-TEXT

Universally Expressive Communication in Multi-Agent Reinforcement Learning

Universally Expressive Communication in Multi-Agent Reinforcement Learning

Poster presented at the Deep Learning Indaba 2022 by Matthew Morris

Emergent Communication in Multi-Agent Reinforcement Learning for Flying Base Stations

Sociolinguistic Study of Gender and Health Literacy in Doctor–Patient Communication in Ilorin Hospitals

Doctor–patient communication is widely recognised as a fundamental component of effective healthcare

Multi-Agent Systems Requirements Analysis for Patient-centered Healthcare Consultancy Service

Agents are currently being discussed in nearly every domain of science and engineering. Through the

Necroraijin/AEGIS-AI-Doctor-Assistant-App

AEGIS is a multi-agent AI Clinical Assistant built with Flutter and Med-Gemma 4B to help doctors dia

ENHANCING HEALTHCARE COMMUNICATION: A YORUBA-TO-ENGLISH NLP MODEL FOR DOCTOR-PATIENT INTERACTIONS USING MACHINE TRANSLATION AND SPEECH-TO-TEXT

The problem of language barriers in healthcare is another pro