A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
出版年份 2021 全文链接
标题
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
作者
关键词
-
出版物
Scientific Reports
Volume 11, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-03-04
DOI
10.1038/s41598-021-84698-5
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions
- (2020) H.A. Haenssle et al. ANNALS OF ONCOLOGY
- Can clinical decision making be enhanced by artificial intelligence?
- (2019) M. Janda et al. BRITISH JOURNAL OF DERMATOLOGY
- Impact of the rise of artificial intelligence in radiology: What do radiologists think?
- (2019) Q. Waymel et al. Diagnostic and Interventional Imaging
- Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study
- (2019) Philipp Tschandl et al. LANCET ONCOLOGY
- Artificial intelligence and the future of psychiatry: Insights from a global physician survey
- (2019) P. Murali Doraiswamy et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over
- (2019) Jasper van Hoek et al. EUROPEAN JOURNAL OF RADIOLOGY
- Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists
- (2018) H A Haenssle et al. ANNALS OF ONCOLOGY
- Medical students' attitude towards artificial intelligence: a multicentre survey
- (2018) D. Pinto dos Santos et al. EUROPEAN RADIOLOGY
- Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs
- (2018) Zhixi Li et al. OPHTHALMOLOGY
- The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program
- (2018) Fernando Collado-Mesa et al. Journal of the American College of Radiology
- Influence of Artificial Intelligence on Canadian Medical Students' Preference for Radiology Specialty: A National Survey Study
- (2018) Bo Gong et al. ACADEMIC RADIOLOGY
- An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs
- (2018) Zhixi Li et al. DIABETES CARE
- Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery
- (2018) Shane O'Sullivan et al. International Journal of Medical Robotics and Computer Assisted Surgery
- The Moral Machine experiment
- (2018) Edmond Awad et al. NATURE
- High-performance medicine: the convergence of human and artificial intelligence
- (2018) Eric J. Topol NATURE MEDICINE
- Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes
- (2017) Daniel Shu Wei Ting et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging
- (2017) Martin Halicek et al. JOURNAL OF BIOMEDICAL OPTICS
- Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
- (2017) Paras Lakhani et al. RADIOLOGY
- Selecting for a sustainable workforce to meet the future healthcare needs of rural communities in Australia
- (2016) M. Hay et al. ADVANCES IN HEALTH SCIENCES EDUCATION
- The End of Radiology? Three Threats to the Future Practice of Radiology
- (2016) Katie Chockley et al. Journal of the American College of Radiology
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