Peer-reviewed research
Speech recognition tools for veterinary case learning: enhancing veterinary education with smartphone-based transcription and AI Summarization
Takuya Yogo, Frontiers in Veterinary Science, 2025 · DOI 10.3389/fvets.2025.1690085
Read the originalRead on October 16, 2026
- What it is
- A small comparative study that transcribed five veterinary ophthalmology case discussions with iPhone-native speech recognition and with OpenAI's Whisper, then rated the transcripts and summaries. It is written as an education and workflow study.
- How much weight it carries
- Peer-reviewed, but very small: five cases and seven raters, in a teaching setting rather than clinical records. It does not test dictation into a veterinary practice system, so treat it as a single data point.
Facts taken from this source
- The veterinary study that compared iPhone speech recognition with Whisper recorded five clinical case discussions.
“Clinical case discussions ( n = 5) were recorded” (Methods)
- Final-year veterinary students and clinicians rated the transcripts and summaries, seven raters in total.
“Final-year veterinary students ( n = 4) and clinicians ( n = 3) evaluated transcripts and summaries” (Methods)
- In the veterinary study, the iPhone's built-in speech recognition gave transcripts that outperformed Whisper on readability, technical accuracy and clinical flow.
“iPhone-native transcription outperformed Whisper in readability, technical accuracy, and clinical flow ( p < 0.05)” (Results)