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We investigated (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20098) AVT systems using surgical case-reports as narratives in a controlled single-speaker evaluation prioritising safety outcomes: specifically 3 commercial transcription-systems (Dragon-Medical-One, Heidi-Health, Tortus); 4 ASR speech-to-text Application Programming Interfaces (Speechmatics-Enhanced, Amazon-Medical-Transcribe, Whisper, GPT4oTranscribe); and an experimental two-stage ASR\u2013Large Language Model (LLM) pipeline incorporating GPT4oTranscribe with GPT-5-LLM generative error correction (GPT4oTranscribe-Corrected-5). Reference-transcripts (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009100; 32,897 words, range\u2009=\u200944\u2013449, mean\u2009=\u2009329\/per-transcript) derived from surgical case-reports were recorded and input into AVT systems as audio-recordings for transcription-output generation. Primary outcome was proportion of transcription-outputs containing at least one clinically significant Class 3 error graded for potential harm. Secondary outcomes included transcription accuracy: Domain-Word-Error-Rate (DWER) against SNOMED-CT, lexical-accuracy (ROUGE score) and semantic similarity (BERT, BART scores). To investigate impact of the experimental pipeline on clinically significant errors, raw ASR and LLM-processed transcription-outputs were compared and errors classified as resolved, remaining or newly introduced. Across (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009800) transcript-outputs, 30\u201368% (GPT4oTranscribe-Corrected-5; Amazon-Medical-Transcribe and Dragon-Medical-One, respectively) contained at least one clinically significant Class 3 error. LLM-processing reduced the proportion of transcript-outputs affected (53% to 30%), amongst 89 clinically significant errors in ASR-output, 51 were resolved, 38 remained significant and 4 were newly introduced. Reference-transcript length significantly increased odds of Class 3 errors for 4 systems (GPT4oTranscribe, Tortus, Amazon-Medical-Transcribe, Dragon-Medical-One; odds ratio 2.65\u20133.34\/additional 100 reference-words). Errors with potential to cause severe harm or death (NHS England Level 3\u20134) totalled (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200953) across systems, concentrated in the domains of medication-type\/dose (39.6%), and investigations\/laboratory results (30.2%). Performance varied across systems for transcription metrics (\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u20090.001). GPT4oTranscribe-Corrected-5 (DWER\u2009=\u20093.60%) and Heidi-Health (DWER\u2009=\u20095.67%) performed best for medical terminology; Amazon-Medical-Transcribe worst (DWER\u2009=\u200924.03%). For lexical and semantic similarity GPT4oTranscribe-Corrected-5 performed best, followed by Heidi-Health. The ASR\u2013LLM pipeline reduced clinically significant errors but did introduce new ones in 3% of transcript-outputs. The safety and effectiveness of these systems in clinical practice requires further evaluation.\n                  <\/jats:p>","DOI":"10.1007\/s10916-026-02471-5","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T06:54:10Z","timestamp":1791528850000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Controlled Single-Speaker Evaluation of Ambient Voice Technologies\u2019 Speech-to-Text Function Using Narrated Surgical Case-Reports"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7287-364X","authenticated-orcid":false,"given":"Ruairi","family":"O\u2019Kane","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1096-5012","authenticated-orcid":false,"given":"Daniel","family":"Stonehouse-Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melody","family":"Shirazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Hutchinson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morgan","family":"Gregg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riya","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-1580","authenticated-orcid":false,"given":"Jadbinder","family":"Seehra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1968-3326","authenticated-orcid":false,"given":"Spyridon N","family":"Papageorgiou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2857-0315","authenticated-orcid":false,"given":"Martyn T","family":"Cobourne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"issue":"1","key":"2471_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s10916-025-02157-4","volume":"49","author":"A Bracken","year":"2025","unstructured":"Bracken, A., Reilly, C., Feeley, A., Sheehan, E., Merghani, K., & Feeley, I. 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