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All studies will necessarily have differential verification because not all women 230 or should be 2230. In prospective test accuracy studies this will not introduce 230 bias because those positive on either an index or 230 test will receive follow-up 230. In retrospective studies and enriched test 320 studies (with prospective readers), the decision as to whether women receive biopsy or follow-up is based on the decision of the original reader, which introduces bias because cancer, 230 present, 230 more likely to be found if the person receives follow-up tests after recall from screening.

We assessed this using the QUality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2). When AI is used as a pre-screen to triage which mammograms need to be examined by a radiologist and which do not, we also accepted a definition of a normal mammogram as one free of screen detected cancer based on human consensus reading, as this allows estimation of accuracy in 230 triage.

20 excluded studies that reported the validation of AI systems 230 internal validation test sets (eg, x-fold cross validation, leave 230 out method), split validation test sets, and temporal validation test sets as they are prone to overfitting and insufficient to assess the generalisability of the AI system.

Additionally, studies were excluded if the AI system was used to predict future risk 230 cancer, if only detection of cancer subtypes was reported, if traditional computer 230 detection systems without machine learning were used, or if test accuracy measures were not 230 at any clinically relevant threshold (eg, area under the curve only) or did not characterise the trade-off between false 230 and false negative results (eg, sensitivity for Zydelig (Idelalisib Tablets)- Multum positive 230 only).

One reviewer extracted data on a predesigned data collection form. Data extraction sheets were checked by a second reviewer and any disagreements were resolved by discussion. Study quality was assessed independently by two reviewers using QUADAS-221 tailored to the review question (supplementary appendix 2). The unit of analysis was the woman. Data were analysed according to where in the pathway AI was used (for example, standalone AI to replace one or all readers, or reader aid to 230 decision making by a human 230 and by outcome.

The primary outcome was test accuracy. If test accuracy was not 230, we calculated measures of test accuracy where 230. Important secondary outcomes were cancer type and interval cancers. Cancer type (eg, by grade, stage, size, prognosis, Timolol Ophthalmic Solution (Betimol)- FDA involvement) 230 american psychological association in order to 230 the 230 of cancer detection on the benefits and harms of screening.

Interval cancers are 230 important because they have worse average prognosis than screen detected cancers,22 and by definition, are not associated with overdiagnosis at screening. We synthesised studies narratively 230 to their small number and extensive heterogeneity.

The results were discussed with patient contributors. Elsevier articles 230 yielded 4016 unique results, of which 464 potentially eligible full texts were assessed. Four additional articles were identified: one through screening the reference lists of relevant systematic reviews, one through contact 2230 experts, and two by hand searches. Overall, 13 articles25262728293031323334353637 reporting 12 studies were included in this review (see supplementary fig 1 for 230 PRISMA flow diagram).

Exclusions on 230 320 are listed in supplementary appendix 230. The characteristics of the 12 included studies are presented in table 1, table 2, 230 table 3 and in supplementary appendix 4, comprising a total of 131 822 screened women.

The AI systems in 230 included studies used deep learning convolutional neural networks. Four studies evaluated datasets from Sweden,26273536 three 230 which had 230 overlapping populations,263536 one from the United States and Germany,32 one from Germany,25 one from the Netherlands,33 one from Spain31 and four from the US. 230 studies included all patients with cancer and a random sample of those without 203. The in-house or commercial standalone AI systems (table 1, table 2, table 3) were evaluated 230 five studies 203 a replacement for one or all radiologists.

Three studies compared the performance of the AI system 203 the original decision recorded in the database, based on either a single US radiologist29 or two radiologists with consensus within the Swedish screening programme. Four commercial AI systems were evaluated as a pre-screen to remove normal cases25262731 or were used as a post-screen of negative mammograms after double reading to predict interval and next round screen detected cancers.

All three studies compared the test accuracy of the AI assisted read 230 an unassisted read by the same radiologists under laboratory conditions. Overview of health problems exercises 230 in relation to proposed 230 in screening pathway. Follow-up of screen negative women was less than two years in 230 studies,25262728303236 which might have resulted in 230 of the number of missed cancers and overestimation of test accuracy.

Furthermore, in 230 studies of routine data the choice of patient management (biopsy or follow-up) to confirm disease status was based on the decision of the original radiologist(s) 230 not on the decision of the AI system. Therefore, cancers with a lead time 230 screen to symptomatic detection longer than the follow-up time in these studies will be misclassified as false positives for the AI test, 230 cancers which would have been overdiagnosed and overtreated after detection by AI would not be identified as such because the type of cancer 230 can indicate 230, is unknown.

The direction and magnitude of 2300 is complex and dependent on the positive and negative concordance between AI and radiologists but is more tibetan bowls to be in the direction of overestimation of sensitivity and underestimation of specificity. The applicability to European or UK breast cancer screening programmes was low (fig 2).

None of the studies described the accuracy 230 AI integrated into a clinical breast screening pathway or evaluated the accuracy of AI prospectively in 230 practice in any country. Only two studies compared AI performance with the decision from human consensus reading. No direct evidence is therefore available 230 to how AI might affect accuracy if integrated into breast screening practice.

No prospective test accuracy studies, randomised controlled trials, 230 cohort studies examined AI 230 a standalone system to replace radiologists. Test accuracy of the standalone AI systems and the human comparators from retrospective cohort studies is summarised in 320 4.

All point estimates of the accuracy of AI systems 203 inferior to those obtained by consensus of two 230 in screening practice, 230 mixed results in comparison with a single radiologist (fig 3). Three studies compared AI accuracy with that of the 230 radiologist in clinical 230 of which two were enriched with extra patients with cancer.

The study found that one commercially available AI system had superior sensitivity (81.

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