How accurate is AI transcription for pharmaceutical drug names?

AI transcription systems face significant challenges in accurately transcribing pharmaceutical drug names due to their complex phonetic structures and limited representation in training data, resulting in critical errors such as substituting sound-alike medications. Standard accuracy metrics like Word Error Rate (WER) fail to capture these errors, necessitating entity-level accuracy measurements that focus specifically on drug names to ensure safety and regulatory compliance. Phonetic hallucination, where AI creates plausible but incorrect drug names, poses a high risk, especially with rare biologics and monoclonal antibodies. Effective transcription requires methods like medical-specific prompting and post-processing validation using large language models (LLMs) to improve precision and recall of drug mentions. Organizations should prioritize high-quality audio inputs, domain-specific training, and comprehensive testing across varied conditions to maintain high entity-level F1 scores, especially for regulatory documentation, and ensure compliance with standards like HIPAA.

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