Hybrid-Code v2: Zero-Hallucination Clinical ICD-10 Coding via Neuro-Symbolic Verification and Automated Knowledge Base Expansion

Zero hallucination is achievable in AI medical coding: Hybrid-Code v2 demonstrates that a system can eliminate invalid code generation entirely (0% Type-I hallucination) while maintaining strong performance (92% precision, 85% coverage), addressing a critical safety concern where neural AI systems previously generated false codes at rates of 6-18%.

Combining AI with rule-based verification creates safer systems: The framework uses neural networks to generate candidate codes but adds a symbolic verification layer that checks format, evidence grounding, negation, timing, and exclusion rules—showing that hybrid approaches can preserve AI's strengths while adding human-interpretable safety guardrails.

Automated knowledge base expansion solves scalability without sacrificing safety: Rather than requiring manual curation of all medical coding rules, the system automatically extracts and validates coding patterns from unlabeled clinical text, achieving 40% better coverage than traditional rule-based systems while maintaining the safety guarantees that make it suitable for clinical use.

Formal safety guarantees matter in healthcare AI: The paper emphasizes that Hybrid-Code v2 provides "formal safety guarantee for syntactic validity," meaning the system's safety properties can be mathematically proven rather than just empirically demonstrated—a critical distinction for high-stakes medical applications where failures directly impact patient care.

Performance and safety are not mutually exclusive: The system outperforms or matches specialized medical AI models (ClinicalBERT, BioBERT) and GPT-4 while eliminating hallucination, demonstrating that responsible AI design doesn't require accepting lower accuracy as the cost of safety.