UltraReporter for transforming spoken diagnostic cues into structured ultrasound reports with large language models

Abstract

Abstract Ultrasound reporting remains a manual, time-consuming process prone to errors and variability. We present UltraReporter, a compact 8B-parameter LLM pipeline that converts real-time spoken cues into structured ultrasound reports. To overcome data scarcity, we developed a multi-agent framework for synthesizing high-quality Chinese cue-report pairs from unpaired narratives. The model was further refined through template-augmented fine-tuning and defect-oriented preference optimization to ensure institutional consistency and minimize hallucinations. Evaluated on 1,311 gold-standard cases, UltraReporter outperformed nine state-of-the-art LLMs, achieving superior clinical scores (e.g. accuracy: 4.82) and NLG metrics (BLEU-4: 89.42). In a blinded reader study, its reports surpassed chief physicians in quality across normal, common, and rare cases, and 72% of prospective reports were deemed equivalent to original ones. UltraReporter integrates seamlessly into clinical workflows, generating ready-to-use reports in 1 second, significantly reducing documentation burden and demonstrating strong potential for clinical integration.