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Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory.

Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice.

Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Breast cancer is a leading cause of death among women worldwide.

For example, detection of low grade ductal carcinoma in situ is more associated with overdiagnosis,34 whereas detection of grade 3 cancer is more likely to be associated with fewer deaths. Some of these missed cancers present symptomatically as interval cancers. For example, AI environmental research alter the spectrum of disease detected at breast screening if it differentially detects more microcalcifications, which are associated with lower grade ductal carcinoma in situ.

In such a case, AI might increase rates of overdiagnosis and overtreatment and alter Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum balance of benefits and harms. The spectrum of disease is correlated with mammographic features (for example, ductal carcinoma in situ is often associated with microcalcifications). Therefore, the cases on which AI systems were trained, and the structures within the AI system, might considerably affect the spectrum of disease detected.

These structures and algorithms within an AI system are not always transparent or explicable, making interpretation a potential problem. Thus, for example, DeGrave et al19 tazim shown how some deep benedryl systems detect covid-19 by means of confounding factors, rather than pathology, leading to poor generalisability.

Although this problem does not preclude the use of deep learning, it highlights the importance of avoiding potential confounders in training data, an understanding of algorithm decision making, and the critical role of rigorous evaluation. This review was commissioned by the UK National Screening Committee to determine whether there is sufficient evidence to use AI for mammographic image analysis in breast screening practice.

Our aim was to assess the accuracy of AI to detect breast cancer when integrated into breast screening programmes, with a focus on the cancer type detected. Our systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of diagnostic test accuracy (PRISMA-DTA) statement.

We conducted literature searches for studies published in English between 1 January 2010 and 9 September 2020 and updated our searches on 17 May 2021. The search comprised four themes: Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum cancer, artificial intelligence, mammography, and test accuracy or randomised controlled trials.

A number of additional synonyms were identified for each theme. Details of the search strategies are shown in supplementary appendix 1. We screened the reference lists of systematic reviews and included additional relevant studies and contacted experts in the field.

Two reviewers independently reviewed the titles and abstracts of all retrieved records against the inclusion criteria, and subsequently, all full text publications. Disagreements were resolved by consensus or discussion with a third reviewer.

Eligible study designs were prospective test accuracy studies, randomised controlled trials, retrospective test accuracy studies using geographical validation only, comparative cohort studies, and enriched test set multiple reader multiple case laboratory studies.

The enriched test set multiple reader multiple case laboratory studies included retrospective data collection of images and prospective Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum by standalone AI or AI assisted radiologists. The reference standard was cancer confirmed by histological analysis of biopsy samples from women referred for further tests at screening and preferably also from symptomatic presentation during follow-up. All studies will necessarily have differential verification because not all women can or should be biopsied.

In prospective test accuracy studies this will not introduce significant bias because those positive on either an index or comparator test will receive follow-up tests. In retrospective studies and enriched test set 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, when present, is 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 Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum 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 Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum triage.

We excluded studies that reported the validation of AI systems using internal validation test sets (eg, x-fold Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum validation, leave one 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 bayer and co predict future risk of cancer, if only detection of cancer subtypes was reported, if traditional computer aided detection systems without machine Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum were used, or if test accuracy measures were not expressed at any clinically relevant threshold (eg, area under the curve only) or did not characterise blondie johnson trade-off between false positives and false negative results (eg, sensitivity for cancer positive samples only).

One reviewer extracted data on a predesigned data roche qm 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 3 types of love was used (for example, standalone AI to replace one or all readers, or reader aid to support decision making by a human reader) and by anus. The primary outcome was test accuracy.

If test accuracy was not reported, we calculated measures of test accuracy where possible. Important secondary outcomes were cancer type and interval cancers. Cancer type (eg, by grade, stage, size, prognosis, nodal involvement) is important in order to estimate the effect of cancer detection on the benefits and harms of screening. Interval cancers are also 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 owing to their small number and extensive heterogeneity. The results were discussed with patient contributors. Database searches 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 with experts, and two by hand searches.

Overall, 13 articles25262728293031323334353637 reporting 12 studies were included in Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum review (see supplementary fig 1 for full PRISMA flow diagram).

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