AIMC Topic: Pandemics

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Role of 5G-powered remote robotic ultrasound during the COVID-19 outbreak: insights from two cases.

European review for medical and pharmacological sciences
The 2019 Novel Coronavirus disease (COVID-19) broke out in Wuhan, China in December 2019 and spread throughout the world. Early screening and early diagnosis play key roles in prevention and management of the epidemic. Attention should also be paid t...

Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

The Journal of bone and joint surgery. American volume
BACKGROUND: The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are ...

Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.

Studies in health technology and informatics
The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New...

[Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumula...

AI Surveillance during Pandemics: Ethical Implementation Imperatives.

The Hastings Center report
Artificial intelligence surveillance can be used to diagnose individual cases, track the spread of Covid-19, and help provide care. The use of AI for surveillance purposes (such as detecting new Covid-19 cases and gathering data from healthy and ill ...