Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study

Clinical Snapshot

Key Findings

• P Value: P<0.001 for ASR improvement

• Effect Size: 80.8% reduction in character error rate

• Primary Outcome: Character error rate reduction from 0.344 to 0.066 with contextual system message

• Nnt Or Sensitivity: Qwen2:72b achieved 83.2% accuracy (16.8% error rate) in pathologist evaluation

• Confidence Interval: 95% CI for improved ASR: 0.048-0.084

Clinical Application

Requires significant technical infrastructure and validation before clinical deployment; draft reports need pathologist verification Limited direct applicability due to different multilingual patterns in Australia; would require adaptation for local language combinations and TGA approval for clinical use Pathology departments with multilingual dictation requirements, particularly in multicultural healthcare settings

Abstract

BACKGROUND: Accurate transcription of pathology gross examination dictation is important for clinical documentation, but multilingual dictation remains challenging in settings where clinicians mix Chinese and English while final pathology reports are written in English. OBJECTIVE: This study aimed to evaluate whether a Whisper-based automatic speech recognition (ASR) pipeline guided by contextual system messages and combined with open-source large language models (LLMs; Qwen2:72b, Llama3.1:70b, Gemma2:27b) could improve multilingual (Chinese-English) pathology dictation transcription accuracy and generate clinically appropriate English gross description reports. METHODS: We conducted a controlled proof-of-concept study using 125 simulated mixed Chinese-English pathology gross examination audio recordings created by physicians or pathologists. Audio recordings were transcribed using Whisper ASR with and without a contextual system message. The ASR transcripts were then converted into English gross description reports using 3 open-source LLMs: Qwen2:72b, Llama3.1:70b, and Gemma2:27b. Outcomes included character error rate, Bilingual Evaluation Understudy, Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, ROUGE-L, Metric for Evaluation of Translation with Explicit Ordering, pathologist Win-Tie-Lose rankings, report-level error categories, inference time, and interrater agreement. RESULTS: The ASR contextual system message reduced the mean character error rate from 0.344 (SD 0.176; 95% CI 0.313-0.375) to 0.066 (SD 0.100; 95% CI 0.048-0.084; P<.001). Qwen2:72b achieved the highest automated metric scores, including a Bilingual Evaluation Understudy of 0.644 (SD 0.307), ROUGE-1 of 0.866 (SD 0.163), ROUGE-2 of 0.771 (SD 0.235), ROUGE-L of 0.842 (SD 0.178), and Metric for Evaluation of Translation with Explicit Ordering of 0.805 (SD 0.214). Pathologist-coded total error rates were 16.8% (21/125) for Qwen2:72b, 45.6% (57/125) for Llama3.1:70b, and 92.8% (116/125) for Gemma2:27b. The exact agreement between the 2 pathologists across full ranking categories was 76.8% (96/125; Cohen κ=0.668), and agreement on the top-ranked model or tied top group was 81.6% (102/125; Cohen κ=0.722). CONCLUSIONS: In this proof-of-concept evaluation, contextual prompting improved ASR transcription accuracy, and Qwen2:72b generated the most accurate English pathology reports among the evaluated LLMs. However, the study used simulated audio recordings, a local vocabulary prompt, and report-level rather than term-level clinical annotation. LLM-generated reports should therefore be considered draft documentation requiring pathologist verification, and prospective validation in real clinical workflows is needed before clinical deployment.

References

1. 1.Lin, K. H., Chang, C. P., Kuo, C. T., Hsu, C. Y., Hung, S. H., Lien, C. Y., Lee, S. H., Yeh, Y. C., & Chu, Y. C. (2026). Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study. JMIR Formative Research. https://doi.org/10.2196/90814