16/04/2026
The science is clear: human oversight in AI clinical documentation isn't optional - it's essential.
A landmark systematic review published this month in BMC Medical Informatics and Decision Making (Ng et al., July 2025) analysed 29 studies evaluating AI-based speech recognition and transcription tools across clinical settings worldwide.
The findings are striking. Word error rates ranged from under 1% in controlled, single-speaker settings to over 50% in real-world conversational and multi-speaker scenarios - precisely the conditions found in specialist outpatient consultations. The authors concluded that even the most advanced LLM-based systems "still generally require human review to ensure clinical safety."
This is not a limitation that technology alone will solve in the short term. It reflects the inherent complexity of clinical conversation: accented speech, overlapping dialogue, dense specialist terminology, and the high stakes of getting it right.
Edify Medical's Dict8ion platform has adopted a deliberately risk-averse approach to AI-assisted clinical documentation. Rather than positioning AI as a replacement for human judgement, we built our hybrid AI-human workflow from the ground up with this evidence in mind. AI handles the heavy lifting - speed, consistency, scalability - while trained human reviewers provide the clinical accuracy layer that the research consistently shows is still necessary.
This isn't a compromise. It's the evidence-based design choice.
As Australia's regulatory and governance frameworks for AI in healthcare continue to mature - including through the Australian Digital Health Agency's newly established AI Enabled Care Expert Advisory Group - the distinction between tools that embed human oversight and those that don't will become increasingly important.
We're proud to be on the right side of that line.
📄 Ng JJW et al. "Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a systematic review." BMC Medical Informatics and Decision Making, 25, 236 (2025).
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-025-03061-0
