Speech Recognition in the Radiology Department: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it