Gams Matjaž, Horvat Tadej, Kolar Žiga, Kocuvan Primož, Mishev Kostadin, Misheva Monika Simjanoska
Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
Applied Artificial Intelligence, Alma Mater Europaea University, Slovenska Street 17, 2000 Maribor, Slovenia.
Healthcare (Basel). 2025 Jul 29;13(15):1843. doi: 10.3390/healthcare13151843.
: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. : HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. : During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, = 0.0135). : Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts.
近年来，对可及且可靠的数字健康服务的需求显著增加，尤其是在面临医生短缺的地区。HomeDOCtor是斯洛文尼亚开发的一个对话式人工智能平台，它通过一种结合了检索增强生成（RAG）和基于Redis的精选医学指南向量数据库的全国适应性架构来满足这一需求。本研究的目的是评估HomeDOCtor在提供人工智能驱动的医疗保健援助方面的性能和影响。
HomeDOCtor旨在实现以用户为中心的沟通和临床相关性，支持多语言和多媒体的市民输入，并且全天候可用。它使用了一组100个国际临床案例和卢布尔雅那大学的150个内科考试问题进行测试，以验证其临床性能。
在为期六个月的全国部署期间，HomeDOCtor收到了绝大多数积极的用户反馈，批评极少，并且超出了最初的预期，尤其是考虑到广泛的媒体报道都在警告人工智能的风险。HomeDOCtor自主提供本地化的、基于证据的指导，包括自我护理说明和转诊建议，平均响应时间在三秒以内。在国际基准测试中，该系统实现了≥95%的Top-1诊断准确率，与领先的医学人工智能平台相当，并且在国内背景下显著优于独立的ChatGPT-4o（90.7%对80.7%，P = 0.0135）。
实际上，HomeDOCtor通过为市民提供全天候的自主、个性化分诊和针对不太复杂医疗问题的自我护理指导，减轻了医疗保健专业人员的负担，确保这些病例得到有效自我管理。该系统还识别出否则可能被忽视的更严重病例，并将它们引导至专业人员处接受适当治疗。从理论上讲，HomeDOCtor表明特定领域、全国适应性的大语言模型可以优于通用模型。在方法论上，它提供了一个在医疗保健中集成符合GDPR的人工智能解决方案的框架。这些发现强调了在不同国家背景下对话式人工智能和远程医疗解决方案中本地化的价值。
