Hybrid AI Medical Diagnosis Expert System: Practical Integration of Rule Engines, Knowledge Retrieval, and Large Language Models

The expert-system-medical-diagnosis project builds an innovative hybrid AI architecture that combines traditional rule engines, retrieval-augmented generation (RAG) technology, and large language models. It aims to address core issues of accuracy, interpretability, and credibility in the medical diagnosis field, providing an intelligent assistant tool for clinical decision-making. This architecture balances the interpretability of traditional expert systems and the powerful capabilities of deep learning models, making it an important exploration in the AI medical field.

Artificial intelligence is widely applied in the medical diagnosis field (e.g., image recognition, pathological analysis). However, the medical field imposes strict requirements on systems: accurate and reliable results, transparent and interpretable decisions, and compliance with ethical regulations. Traditional expert systems have strong interpretability but struggle to handle complex unstructured data; pure deep learning models have strong performance but are "black boxes". How to combine the advantages of both to build a trustworthy system is an important topic in current AI medical research, which led to the proposal of the expert-system-medical-diagnosis project.

The core innovation of the project is a three-layer architecture:

1. Rule Engine Layer: Encapsulates deterministic rules verified by medical experts to quickly provide definite diagnostic suggestions;
2. Retrieval-Augmented Knowledge Base Layer: Integrates structured medical knowledge and multi-source information (literature, cases, etc.) to provide contextual support for complex cases;
3. Large Language Model Reasoning Layer: Processes natural language input, integrates outputs from the previous two layers, generates diagnostic reports, and interacts naturally with doctors.

• Rule Engine: Uses a declarative rule language, allowing medical experts to naturally express diagnostic rules; the rule base is organized in layers, with confidence scores and evidence sources attached for easy update and verification.
• Retrieval Augmentation: The knowledge base integrates multi-source medical information; relevant knowledge fragments are extracted via vector databases and semantic search to serve as LLM context, ensuring knowledge timeliness and traceability.
• Large Language Model: Acts as a coordinator, reasoning by synthesizing rules and retrieval results; generates structured reports (including conclusions, reasoning processes, confidence levels, etc.) through prompt engineering, and supports natural language interaction to explain diagnostic thinking.

The system ensures interpretability and credibility through multiple methods:

• Diagnostic results are accompanied by detailed reasoning chains (triggered rules, retrieved knowledge, LLM logic);
• A confidence scoring mechanism that recommends manual review when scores are below the threshold;
• A counterfactual reasoning function that supports doctors in asking "how changes in indicators affect diagnosis" to assist clinical decision-making.

The system has a wide range of application scenarios:

• Primary care: As a doctor's assistant to reduce missed diagnoses and misdiagnoses;
• Medical education: Interpretability helps students understand diagnostic reasoning;
• Grassroots medical care: Compensates for the shortage of doctors in resource-poor areas;
• Specialist fields: Provides comprehensive support in combination with image analysis. It always emphasizes assisting rather than replacing human doctors' decisions.

Ethical aspects: Adopts privacy protection technologies, establishes fairness assessment mechanisms, and clarifies system usage boundaries. Future outlook: Improvements in LLM capabilities and knowledge base perfection will enhance system intelligence; the human-machine collaboration model will evolve into an intelligent partner. The project provides a reference implementation for the responsible application of AI in healthcare and promotes technology deployment.