نتائج البحث

Artificial intelligence in musculoskeletal applications: a primer for radiologists.

• Authors : Tong, Michelle; Zhou, Jiamin; Akkaya, Zehra

Subjects: Artificial intelligence; deep learning; machine learning

• Source: Diagnostic and Interventional Radiology. 31(2)

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Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

• Authors : Miyazaki A; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.; Ikejima K

Subjects: COVID-19*/COVID-19*/COVID-19*/diagnostic imaging ; Deep Learning* ; Pneumonia*/Pneumonia*/Pneumonia*/diagnostic imaging

• Source: Scientific reports [Sci Rep] 2023 Oct 16; Vol. 13 (1), pp. 17533. Date of Electronic Publication: 2023 Oct 16.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort.

• Authors : Kim EY; Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea.; Kim YJ

Subjects: Deep Learning* ; Radiologists*; Radiography, Thoracic/Radiography, Thoracic/Radiography, Thoracic/*methods

• Source: PloS one [PLoS One] 2022 Feb 24; Vol. 17 (2), pp. e0264383. Date of Electronic Publication: 2022 Feb 24 (Print Publication: 2022).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

• Authors : Helwan A; Lebanese American University, School of Engineering, Department of ECE, Byblos, Lebanon.; Ma'aitah MKS

Subjects: Deep Learning* ; Radiologists*/Radiologists*/Radiologists*/statistics & numerical data ; SARS-CoV-2*

• Source: Computational and mathematical methods in medicine [Comput Math Methods Med] 2021 May 10; Vol. 2021, pp. 5527271. Date of Electronic Publication: 2021 May 10 (Print Publication:Publisher: Hindawi Country of Publication: United States NLM ID: 101277751 Publication Model: eCollection Cited Medium: Internet ISSN: 1748-6718

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Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.

• Authors : Anderson PG; Imagen Technologies, 224 W 35th St Ste 500, New York, NY, 10001, USA. .; Tarder-Stoll H

Subjects: Deep Learning* ; Radiography, Thoracic*/Radiography, Thoracic*/Radiography, Thoracic*/methods; Humans

• Source: Scientific reports [Sci Rep] 2024 Oct 24; Vol. 14 (1), pp. 25151. Date of Electronic Publication: 2024 Oct 24.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Should we replace radiologists with deep learning? Pigeons, error and trust in medical AI.

• Authors : Alvarado R; Department of Philosophy, University of Oregon, Eugene, Oregon.

Subjects: Artificial Intelligence* ; Deep Learning*; Animals

• Source: Bioethics [Bioethics] 2022 Feb; Vol. 36 (2), pp. 121-133. Date of Electronic Publication: 2021 Oct 18.Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 8704792 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning

• Authors : Jandee Lee; Jung Hyun Yoon; Eunjung Lee

Subjects: Male; Adult; Medicine (General)

• Source: J Adv ResJournal of Advanced Research, Vol 62, Iss, Pp 219-228 (2024)

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Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

• Authors : Rajpurkar P; Department of Computer Science, Stanford University, Stanford, California, United States of America.; Irvin J

Subjects: Clinical Competence* ; Deep Learning* ; Radiologists*

• Source: PLoS medicine [PLoS Med] 2018 Nov 20; Vol. 15 (11), pp. e1002686. Date of Electronic Publication: 2018 Nov 20 (Print Publication: 2018).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101231360 Publication Model: eCollection Cited Medium: Internet

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Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights.

• Authors : Talaat, Fatma M.^1,2 (AUTHOR) ; Gamel, Samah A.³ (AUTHOR) ; El-Balka, Rana Mohamed^4,5 (AUTHOR)

• Source: Cancers. Nov2024, Vol. 16 Issue 21, p3668. 20p.

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Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?

• Authors : Arslan, Aydan¹ (AUTHOR); Alis, Deniz² (AUTHOR) ; Erdemli, Servet² (AUTHOR)

• Source: Insights into Imaging. 3/20/2023, Vol. 14 Issue 1, p1-10. 10p.

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