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Review
. 2022 Dec 23;13(1):45.
doi: 10.3390/diagnostics13010045.

The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis

Dilber Uzun Ozsahin  1   2 , Declan Ikechukwu Emegano  2 , Berna Uzun  2   3   4 , Ilker Ozsahin  2   5
Affiliations
Review

The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis

Dilber Uzun Ozsahin et al. Diagnostics (Basel). .

Abstract

Several studies have demonstrated the value of artificial intelligence (AI) applications in breast cancer diagnosis. The systematic review of AI applications in breast cancer diagnosis includes several studies that compare breast cancer diagnosis and AI. However, they lack systematization, and each study appears to be conducted uniquely. The purpose and contributions of this study are to offer elaborative knowledge on the applications of AI in the diagnosis of breast cancer through citation analysis in order to categorize the main area of specialization that attracts the attention of the academic community, as well as thematic issue analysis to identify the species being researched in each category. In this study, a total number of 17,900 studies addressing breast cancer and AI published between 2012 and 2022 were obtained from these databases: IEEE, Embase: Excerpta Medica Database Guide-Ovid, PubMed, Springer, Web of Science, and Google Scholar. We applied inclusion and exclusion criteria to the search; 36 studies were identified. The vast majority of AI applications used classification models for the prediction of breast cancer. Howbeit, accuracy (99%) has the highest number of performance metrics, followed by specificity (98%) and area under the curve (0.95). Additionally, the Convolutional Neural Network (CNN) was the best model of choice in several studies. This study shows that the quantity and caliber of studies that use AI applications in breast cancer diagnosis will continue to rise annually. As a result, AI-based applications are viewed as a supplement to doctors' clinical reasoning, with the ultimate goal of providing quality healthcare that is both affordable and accessible to everyone worldwide.

Keywords: artificial intelligence; breast cancer; diagnosis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram of AI in BC diagnosis.
Figure 2
Figure 2
Showing publications on AI in breast cancer diagnosis between 2012–2022.
Figure 3
Figure 3
Size of input or training data used by the different countries where the studies were carried out.
Figure 4
Figure 4
Summary of task, date, and number of images or studies used in figure.
Figure 5
Figure 5
Relationship between AI, ML, and DL.
See this image and copyright information in PMC

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