2023
DOI: 10.3390/jcm12020419
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Overview of Artificial Intelligence in Breast Cancer Medical Imaging

Abstract: The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In ad… Show more

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Cited by 34 publications
(21 citation statements)
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“…This section underscores how AI assists in the automated detection and classification of breast tumours, contributing to improved diagnostic accuracy and reduced false positives. By efficiently processing and analysing vast quantities of medical images, AI empowers radiologists and clinicians to make more precise and timely decisions, enhancing patient outcomes and minimising unnecessary interventions [ 57 ].…”
Section: Reviewmentioning
confidence: 99%
“…This section underscores how AI assists in the automated detection and classification of breast tumours, contributing to improved diagnostic accuracy and reduced false positives. By efficiently processing and analysing vast quantities of medical images, AI empowers radiologists and clinicians to make more precise and timely decisions, enhancing patient outcomes and minimising unnecessary interventions [ 57 ].…”
Section: Reviewmentioning
confidence: 99%
“…The limitations of AI-based diagnosis are (1) Lack of generalization in the methodologies used in AI to produce reproducible results; (2) Algorithms with reduced false positive rates and high specificity are essential to deal with image data obtained from different modalities and patient independent variations; and (3) The applicability of AI in diagnosis is to be validated in real-time through clinical trials on a large sample size before adopting to the clinical practice. 41 DL systems based on breast US images have improved diagnostic accuracy, minimized interpretation time, and reduced the number of call-backs and biopsies. The performance of AI CAD is better when compared to radiologists with less experience.…”
Section: Clinical Potential Challenges and Impact Of Ai-assisted Brea...mentioning
confidence: 99%
“…The limitations of AI-based diagnosis are (1) Lack of generalization in the methodologies used in AI to produce reproducible results; (2) Algorithms with reduced false positive rates and high specificity are essential to deal with image data obtained from different modalities and patient independent variations; and (3) The applicability of AI in diagnosis is to be validated in real-time through clinical trials on a large sample size before adopting to the clinical practice. 41…”
Section: Clinical Potential Challenges and Impact Of Ai-assisted Brea...mentioning
confidence: 99%
“…Concerns about the influence on the workforce, ethics, legislation, and standardization are common, as are implementation costs and problems with data standards. We must acknowledge and tackle these constraints to integrate AI responsibly in the treatment of breast cancer [ 31 , 32 ].…”
Section: Reviewmentioning
confidence: 99%