Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/12/3111 |
| _version_ | 1797460662354444288 |
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| author | Xiao Jian Tan Wai Loon Cheor Li Li Lim Khairul Shakir Ab Rahman Ikmal Hisyam Bakrin |
| author_facet | Xiao Jian Tan Wai Loon Cheor Li Li Lim Khairul Shakir Ab Rahman Ikmal Hisyam Bakrin |
| author_sort | Xiao Jian Tan |
| collection | DOAJ |
| description | Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest. |
| first_indexed | 2024-03-09T17:08:14Z |
| format | Article |
| id | doaj.art-4c7e333221584a25845d2ad77f0887a1 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T17:08:14Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-4c7e333221584a25845d2ad77f0887a12023-11-24T14:18:52ZengMDPI AGDiagnostics2075-44182022-12-011212311110.3390/diagnostics12123111Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella ReviewXiao Jian Tan0Wai Loon Cheor1Li Li Lim2Khairul Shakir Ab Rahman3Ikmal Hisyam Bakrin4Centre for Multimodal Signal Processing, Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, Kuala Lumpur 53300, MalaysiaTE Connectivity Operations Sdn. Bhd, Perai 13600, MalaysiaCentre for Multimodal Signal Processing, Tunku Abdul Rahman University of Management and Technology (TAR UMT), Jalan Genting Kelang, Setapak, Kuala Lumpur 53300, MalaysiaDepartment of Pathology, Hospital Tuanku Fauziah, Jalan Tun Abdul Razak, Kangar 01000, MalaysiaDepartment of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM Serdang, Serdang 43400, MalaysiaArtificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.https://www.mdpi.com/2075-4418/12/12/3111artificial intelligencedeep learningmachine learningbreast imagingmammogramscientometric analysis |
| spellingShingle | Xiao Jian Tan Wai Loon Cheor Li Li Lim Khairul Shakir Ab Rahman Ikmal Hisyam Bakrin Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review Diagnostics artificial intelligence deep learning machine learning breast imaging mammogram scientometric analysis |
| title | Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review |
| title_full | Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review |
| title_fullStr | Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review |
| title_full_unstemmed | Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review |
| title_short | Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review |
| title_sort | artificial intelligence ai in breast imaging a scientometric umbrella review |
| topic | artificial intelligence deep learning machine learning breast imaging mammogram scientometric analysis |
| url | https://www.mdpi.com/2075-4418/12/12/3111 |
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