Speech recognition tools for veterinary case learning: enhancing veterinary education with smartphone-based transcription and AI Summarization — a comparative study of workflow and usability - Yogo, Takuya (Frontiers in Veterinary Science, 2025)
BackgroundAccurate 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.ObjectivesThis 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.MethodsClinical 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.ResultsiPhone-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.ConclusionSmartphone-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.