Speech Recognition in the Radiology Department: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of the literature describing the impact of speech recognition systems on report error rates and productivity in radiology departments. METHODS: The search was conducted for relevant papers published from January 1992 to October 2013. Comparative studies reporting any of the following outcomes were selected: error rates, departmental productivity, and radiologist productivity. The retrieved studies were assessed for quality and risk of bias. RESULTS: The literature search identified 85 potentially relevant publications, but, based on the inclusion and exclusion criteria, only 20 were included. Most studies were before and after assessments with no control group. There was a large amount of heterogeneity due to differences in the imaging modalities assessed and the outcomes measured. The percentage of reports containing at least one error varied from 4.8% to 89% for speech recognition, and from 2.1% to 22% for transcription. Departmental productivity was improved with decreases in report turnaround times varying from 35% to 99%. Most studies found a lengthening of radiologist dictation time. CONCLUSION: Overall gains in departmental productivity were high, but radiologist productivity, as measured by the time to produce a report, was diminished.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".