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Speech recognition tools for veterinary case learning: enhancing veterinary education with smartphone-based
1Department of Veterinary Surgery, Nippon Veterinary and Life Science University, Tokyo, Japan.
Background:
Accurate documentation of clinical teaching sessions is critical, particularly in multilingual contexts. Recent advances in smartphone-based speech recognition and large language models (LLMs) may enhance transcription accuracy, streamline case summarization, and improve usability. However, their comparative performance in veterinary settings remains underexplored.
Objectives:
This study evaluated the quality, usability, and educational value of smartphone-native transcription compared with Whisper-based transcription and AI-assisted summarization in veterinary ophthalmology education.
Methods:
Clinical case discussions (n = 5) were recorded and transcribed using (1) iPhone-native speech recognition and (2) the Whisper automatic speech recognition system. Transcripts were further processed into SOAP-format summaries with and without LLM-based summarization. Final-year veterinary students (n = 4) and clinicians (n = 3) evaluated transcripts and summaries using a 5-point Likert scale across readability, accuracy, clinical clarity, and educational utility. Statistical comparisons were performed using Wilcoxon signed-rank tests.
Results:
iPhone-native transcription outperformed Whisper in readability, technical accuracy, and clinical flow (p < 0.05). AI-assisted SOAP-format summarization improved clarity and perceived learning value but occasionally introduced minor semantic distortions. Clinicians rated AI-enhanced summaries as more concise and educationally useful than raw transcripts. Both students and clinicians reported reduced cognitive load and usability with smartphone-based transcription workflows.
Conclusion:
Smartphone-native transcription combined with AI summarization provides a practical and effective workflow for veterinary education. While Whisper offers cross-device flexibility, its current accuracy in multilingual contexts is limited. Integration of smartphone transcription and LLM summarization may improve documentation, comprehension, and student engagement in clinical teaching.
