TL;DR
A systematic evaluation framework for procedure-code recommendation is established and practical requirements for future systems that are accurate, review-efficient, and grounded in clinical evidence are identified.
This work proposes Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating external ICD coding guidelines as structured knowledge, and enables tight coupling between domain knowledge and LLM reasoning.
Clinico is developed and evaluated, a coding agent that organises evidence by time and clinical relationships to adjudicate diagnoses and update their status during admission and had the highest observed estimates of principal-diagnosis agreement and complete code-set micro-F1 among evaluated methods.
Results show that domain adaptation, task-aligned optimization, and evidence-guided verification can improve both coding accuracy and the auditability of model outputs.
AI-based automatic ICD-10 coding is a promising approach for clinical coding support, and future research should prioritize rare-label imbalance, reproducibility, explainability, and validation across diverse clinical settings.
LLMs represent a viable and flexible approach to diagnosis code extraction from unstructured clinical notes that can augment structured diagnoses and provide contextualizing metadata.
Combining LLM-based clinical interpretation with deterministic terminology verification produces auditable, database-ready code sets, but retrieval and broad family expansion do not consistently improve exact-code performance.
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