Evaluating AI in breast cancer screening: a complex task

Dustler, Magnus

doi:10.1016/s2589-7500(20)30019-4

Cited by 6 publications

(4 citation statements)

References 10 publications

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Section: Discussioncontrasting

confidence: 90%

“…The findings from our systematic review disagree with the publicity some studies have received and opinions published in various journals, which claim that AI systems outperform humans and might soon be used instead of experienced radiologists. 10 11 12 13 Our different conclusion is based on our rigorous and systematic evaluation of study quality. We did not extract the “simulation” parts of studies, which were often used as the headline numbers in the original papers, and often estimated higher accuracy for AI than the empirical data of the studies.…”

Section: Discussionmentioning

confidence: 99%

“…In 2019, 3.8% of all peer reviewed scientific publications worldwide on Scopus related to AI 9. Claims have been made that image recognition using AI for breast screening is better than experienced radiologists and will deal with some of the limitations of current programmes 10111213. For instance, fewer cancers might be missed because an AI algorithm is unaffected by fatigue or subjective diagnosis,1415 and AI might reduce workload or replace radiologists completely 1112…”

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

Freeman

Geppert

Stinton

et al. 2021

BMJ

159

118

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Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. 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. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. 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. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. 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 in 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. Study registration Protocol registered as PROSPERO CRD42020213590.

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Section: Discussioncontrasting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

Freeman

Geppert

Stinton

et al. 2021

BMJ

159

118

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“…AI may be used to overcome the constraints of screening mammography in a variety of ways, including as a detection tool with a human reader. Human-like or superhuman performance has been shown in studies employing different algorithms under circumstances that are sometimes very realistic [103]. Vaka AR [104] using a dataset taken from M.G Cancer Hospital and Research Institute, Visakhapatnam, India.…”

Section: Summary Of Previous Research On Breast Cancer Detection 41 Image Processingmentioning

confidence: 99%

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

et al. 2021

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Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

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A General-Purpose AI Assistant Embedded in an Open-Source Radiology Information System

Purkayastha

Isaac

Anthony

et al. 2023

Artificial Intelligence in Medicine

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Evaluating AI in breast cancer screening: a complex task

Cited by 6 publications

References 10 publications

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

A General-Purpose AI Assistant Embedded in an Open-Source Radiology Information System

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