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Advancements in Oncology with Artificial Intelligence—A Review Article
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In: Cancers; Volume 14; Issue 5; Pages: 1349 (2022)
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Management and Outcome of Young Women (≤40 Years) with Breast Cancer in Switzerland
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In: Cancers; Volume 14; Issue 5; Pages: 1328 (2022)
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Metaphors of cancer in the Arabic language: An analysis of the use of metaphors in the online narratives of breast cancer patients
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In: Open Linguistics, Vol 8, Iss 1, Pp 27-45 (2022) (2022)
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound
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In: Diagnostics; Volume 12; Issue 1; Pages: 66 (2021)
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| 11 |
Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results
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In: Cancers; Volume 13; Issue 24; Pages: 6273 (2021)
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| 12 |
Culture and breast cancer surgical decisions and experiences
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| 13 |
Characterization of circulating breast cancer cells with tumorigenic and metastatic capacity
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In: ISSN: 1757-4676 ; EISSN: 1757-4684 ; EMBO Molecular Medicine ; https://hal.archives-ouvertes.fr/hal-03602489 ; EMBO Molecular Medicine, Wiley Open Access, 2020, 12 (9), pp.e11908. ⟨10.15252/emmm.201911908⟩ (2020)
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Breast cancer patients' language use across four stages ...
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| 15 |
Pattern Recognition of Non-Mass Enhancements on Breast MRI – A Pictorial Review with Radiologic-Pathologic Correlation. ...
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Role of BD4BREAST in supporting the categorization of mammographic findings according to the BI-RADS mammographic lexicon ...
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Can MRI Biomarkers Predict Triple-Negative Breast Cancer?
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In: Diagnostics; Volume 10; Issue 12; Pages: 1090 (2020)
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| 18 |
Exploring Intimacy in Collaborative Photographic Narratives of Breast Cancer
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In: Humanities ; Volume 9 ; Issue 1 (2020)
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| 19 |
Using artificial intelligence to analyse and teach communication in healthcare
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In: Breast (2020)
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Abstract:
Communication is a core component of effective healthcare that impacts many patient and doctor outcomes, yet is complex and challenging to both analyse and teach. Human-based coding and audit systems are time-intensive and costly; thus, there is considerable interest in the application of artificial intelligence to this topic, through machine learning using both supervised and unsupervised learning algorithms. In this article we introduce health communication, its importance for patient and health professional outcomes, and the need for rigorous empirical data to support this field. We then discuss historical interaction coding systems and recent developments in applying artificial intelligence (AI) to automate such coding in the health setting. Finally, we discuss available evidence for the reliability and validity of AI coding, application of AI in training and audit of communication, as well as limitations and future directions in this field. In summary, recent advances in machine learning have allowed accurate textual transcription, and analysis of prosody, pauses, energy, intonation, emotion and communication style. Studies have established moderate to good reliability of machine learning algorithms, comparable with human coding (or better), and have identified some expected and unexpected associations between communication variables and patient satisfaction. Finally, application of artificial intelligence to communication skills training has been attempted, to provide audit and feedback, and through the use of avatars. This looks promising to provide confidential and easily accessible training, but may be best used as an adjunct to human-based training.
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Keyword:
Edited by Nehmat Houssami; Giuseppe Pozzi and Brigitte Seroussi; Maria João Cardoso; Virtual special issue: Artificial Intelligence in Breast Cancer Care
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375542/
http://www.ncbi.nlm.nih.gov/pubmed/32007704
https://doi.org/10.1016/j.breast.2020.01.008
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| 20 |
Genetic testing and eHealth usage among Deaf women.
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In: Journal of genetic counseling, vol 28, iss 5 (2019)
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