TL;DR
This study investigates whether large language models (LLMs) can assist in identifying extraction errors and generating candidate rules to improve symbolic clinical NLP systems and suggests that LLMs can support scalable rule refinement for symbolic clinical NLP systems.
DDx-Finder is presented, an open-source framework that leverages Model Context Protocol (MCP) servers for direct EMR and literature access, enabling prompt-driven clinical state extraction and reliable case-report re- trieval via generating searching query by LLM, while addressing limitations related to resource demands and privacy concerns.
An engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation is introduced.
Overall, the evidence suggests that success in lower capability levels does not consistently translate to higher-level reasoning, especially when reports are inconsistent, required staging inputs are missing, or clinical assumptions must be inferred, which helps explain gaps between benchmark results and practical adoption.
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support system.
A single LLM verifier lacks sufficient reliability to serve as a stand-alone judge of clinical reasoning at scale, and that structured human oversight remains essential.
Open LLMs can extract clinical findings from Finnish pediatric records with accuracy comparable to published English benchmarks, and uncertainty-based triage substantially reduces required expert workload.
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