ArticlePLOS digital health2026
A novel statistical framework for quantifying risks and benefits of AI automation in screening mammography.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- A Detection, Diagnostic, and Triage AI Buyer's Guide: Questions that Matter.Journal of imaging informatics in medicine · 2026Article
- LENS: A mammography-specific hybrid CNN-Transformer with lesion-aware evidence modeling.PloS one · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI has been proposed as a triage or "rule-out" device to reduce radiologist workload, but it is presently unclear how an AI "rule-out" threshold should be determined. We present a framework for determining an optimal threshold. Using a retrospective study design, 114,229 bilateral 2D digital screening mammograms were analyzed from 2006-2023 at a single study site. All mammograms were given an AI score using Mirai, an open-source deep-learning model which provides a 1-year risk score. Several metrics were examined using two thresholds for determining ruled out versus retained cases: 1) Caseload Reduction Rate (CRR; percent of caseload reduced due to rule-out), 2) Gross AI False Omission Rate (G-FOR; probability of a patient having breast cancer if ruled out), 3) AI Net False Omission Rate (N-FOR; probability of a patient having breast cancer if ruled out and the radiologist would have caught in standard care [i.e., no triage]), 4) AI Adjusted Net False Omission Rate (30%) (AN-FOR[30%]; N-FOR adjusted for the hypothetical scenario where radiologists detect an extra 30% of breast cancers among AI retained cases). The two thresholds were risk scores of 0.2 (Youden's J) and 0.05 (AN-FOR[30%]=0). The former is mathematically optimal; the latter reflects a threshold where AI "rule-out" does not introduce any total increase in False Negatives. At the 0.20 threshold, G-FOR, N-FOR, and AN-FOR (30%) are 0.26%, 0.17%, and 0.14%, respectively (223, 141, and 121, respectively, missed cancer cases) and CRR = 75%. At the 0.05 threshold, the G-FOR, N-FOR, and AN-FOR (30%) are 0.12%, 0.07%, and 0.00% (49, 30, and 0, respectively, missed cancer cases) and CRR = 36%. We demonstrate how radiology practices can consider the trade-offs of using different AI scores as "rule-out" thresholds. At the AN-FOR rate of 30%, the Youden's J threshold results in 121 additional missed cancers for a 75% caseload reduction. We estimate no additional missed cancers at a 36% caseload reduction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.