SPEECH RECOGNITION TOOLS FOR VETERINARY CASE LEARNING Enhancing Veterinary Education with Smartphone-Based Transcription and AI Summarization: A Comparative Study of Workflow and Usability

Takuya Yogo^1,2*

• ¹Laboratory of Veterinary Physiology, Laboratory of Veterinary Physiology, School of Veterinary Medicine, Nippon Veterinary and Life Science University, Tokyo, Japan
• ²Nihon Jui Seimei Kagaku Daigaku, Musashino, Japan

Abstract 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. SPEECH RECOGNITION TOOLS FOR VETERINARY CASE LEARNING 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. Conclusions: 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.