How accurate are AI transcripts for technical or medical terms?

AI transcription systems often struggle with accurately rendering technical and medical terms due to their specialized vocabulary, which is phonetically complex and rarely appears in general training data, leading to critical errors like incorrect drug dosages. Standard metrics like Word Error Rate (WER) are insufficient for assessing accuracy in these contexts, as they treat all errors equally, failing to account for the impact of specific terminology mistakes. Instead, metrics such as Missed Entity Rate (MER) provide a clearer understanding of how well models handle essential terms. Tools like AssemblyAI's Universal-3 Pro, which offers features such as Medical Mode and keyterms prompting, enhance transcription accuracy by focusing on domain-specific vocabulary. These features allow for customized prompts and vocabulary injection, improving recognition of critical terms necessary for accurate and reliable transcription in fields like healthcare, legal, and technical domains. The challenge of maintaining accuracy extends to non-English languages and poor-quality audio, where strategic use of transcription features and high-quality input can mitigate errors. As AI transcription technology evolves, the ability to customize and optimize for specific domain needs becomes crucial in bridging the gap between AI and human transcription accuracy.