Automated Software Evaluation in Screening Mammography: A Scoping Review of Image Quality and Technique Assessment
Bibliographic record
Abstract
BACKGROUND: Standardised breast positioning and optimal compression are critical components of effective breast cancer screening. This scoping review aims to report the current landscape of automated software tools developed for image quality assessment and mammographic technique evaluation, and to examine their reported impact. METHODS: A scoping review was undertaken across PubMed (MEDLINE), Scopus, and Emcare. Eligible studies were published between January 2014 and March 2025 and investigated the use of automated software or artificial intelligence-based tools to assess image quality, breast positioning, or compression in mammography or digital breast tomosynthesis. RESULTS: Automated software was predominantly utilised in high-resource settings, where it provided benchmarked feedback, reduced the subjectivity inherent in traditional visual grading systems, and supported radiographer learning and skill development with measurable improvements. However, radiographer training in these systems, the impact of software on clinical workflow, and barriers to implementation, particularly in low-resource settings, were insufficiently addressed in the literature. Furthermore, no studies reported on the relationship between software-generated metrics and breast cancer screening outcomes. CONCLUSIONS: Automated software for image quality evaluation represents a significant advancement in breast screening, illustrating the potential of technology to strengthen the screening-to-treatment continuum in breast cancer care. Nonetheless, widespread adoption requires evidence that these tools directly contribute to improved cancer detection outcomes to justify their uptake.
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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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".