Anatomic Taxonomy-Based Medical Element Recovery from Speech-to-Text AI Transcripts in Radiology Reporting

abstract

Radiology reporting remains a critical bottleneck in diagnostic imaging workflows. While speech-to-text technology has dramatically accelerated dictation, converting unstructured narrative transcripts into queryable clinical data remains manual and error-prone. Transcription errors, anatomic terminology variation, and spatial relationship ambiguity further complicate automated extraction. This paper develops a taxonomy-driven framework for recovering structured medical elements from speech-to-text radiology transcripts by grounding language understanding in formal anatomic ontologies and spatial relationship models. The approach integrates medical NLP with standardised clinical taxonomies (SNOMED, RadLex) and demonstrates how controlled vocabulary recovery can enable reliable downstream tasks — from quality assurance to evidence extraction to epidemiological analysis — without requiring manual correction of transcript errors.

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