HouseGPT: A Multimodal AI Assistant for Medical Diagnosis

HouseGPT is a production-grade multimodal AI assistant built on LangGraph, designed to provide diagnostic support for medical professionals while remaining user-friendly for daily interactions. Its core features include multimodal understanding (integration of text, images, and audio), long-term memory mechanisms, multi-model collaboration strategies, and a human-machine collaboration model. It is positioned as a research and auxiliary tool and does not replace professional medical diagnosis.

Traditional medical AI systems often focus on single tasks (such as image recognition or medical record analysis), while the new generation of systems pursues comprehensive capability integration. HouseGPT was born under this trend as an innovative practice to build a production-ready multimodal AI assistant, exploring its application potential in medical support and daily conversations.

Medical diagnosis requires the integration of multimodal information (text, images, audio). HouseGPT can parse medical records/symptom descriptions (text), analyze medical images (X-rays, CT scans, etc.), and process cardiopulmonary sounds/voice (audio). It achieves information fusion through unified representation learning and cross-modal attention mechanisms, such as the corresponding analysis of lesions and symptoms.

HouseGPT uses the LangGraph framework, whose graph-structured workflow supports flexible design of complex logic. The diagnostic process includes: receiving input → selecting processing nodes → information extraction → knowledge retrieval → generating recommendations. Architectural advantages: modularity for easy testing, visualization for easy debugging, flexible expansion, ensuring interpretability and controllability in medical scenarios.

Long-term memory mechanism: Records past conversations, health history, and preferences, supporting chronic disease management, quick review of follow-up visits, and personalized services. Technically, it combines vector databases (similarity retrieval) and structured storage (precise query). Multi-model collaboration: Dynamically selects models specialized in visual, language, or medical fields to improve professional capabilities and robustness.

Professional mode: Provides diagnostic assistance for medical staff (symptom analysis, image interpretation, literature retrieval, treatment recommendations), which requires review by professionals. Daily mode: For ordinary users, answering health questions, life advice, and casual chats with a friendly and easy-to-understand interface. The two modes switch seamlessly and automatically adjust the response style.

Medical AI needs to pay attention to ethical safety: data privacy protection (secure storage and transmission of information), content filtering (preventing harmful recommendations), human-machine collaboration (key decisions are controlled by professionals), and interpretability (reviewable reasoning process). The project declares itself as a research auxiliary tool and reminds users to seek professional help for health issues.

HouseGPT represents the direction of medical AI: building human-machine collaborative intelligent assistants rather than fully automated diagnosis. Features such as multimodality, long-term memory, and multi-model collaboration enable it to play a valuable role in real medical environments. With the maturity of technology and improvement of regulations, AI will play a more important role in the medical field, benefiting both patients and medical staff.