Background: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. Methods: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models.

As the Covid-19 pandemic continues to evolve, there is a pressing need for a faster diagnostic system. Testing kit shortages, virus mutations, and soaring numbers of cases have overwhelmed health care systems worldwide. Even when a good testing policy is in place, lab testing is arduous, expensive, and time consuming. Cheap antigen tests, which can give results in 30 seconds, are widely available but suffer from low sensitivity; The tests correctly identifying just 75% of Covid-19 cases a week after symptoms start [2]. Shashwat Sanket and colleagues set out to find an easy, fast, and accurate alternative using simple chest X-ray images.

An artificial intelligence voice analysis tool can help diagnose COVID-19 in asymptomatic patients, according to its manufacturer, Vocalis Health. The technology -- called VocalisCheck -- works by comparing a person's voice sample to a COVID-19-positive voice composite. VocalisCheck assesses their risk level of testing positive for COVID-19 and whether they require further testing. According to the company, early study results show that VocalisCheck had a sensitivity of 87% and specificity of of 53%, when used alone, adding "even better" results were achieved when combined with a symptom questionnaire. "Over time, we will collect more and more data, which can strengthen the AI and make the vocal biomarker even more accurate," Shady Hassan, MD, co‐founder, chief medical and chief operating officer of Vocalis Health, told Healio Primary Care.

Although the initial wave of the SARS-CoV-2 pandemic has abated in many countries, healthcare providers are still looking to identify as many COVID-19 patients as possible and contain the disease. Fast and accurate diagnosis is especially important when unsuspecting patients with a coronavirus infection come to the hospital with health complaints but don't yet show symptoms of COVID-19. Nasal swab samples analyzed by RT-PCR are currently recommended for the diagnosis of COVID-19, however, supply shortages, a wait time of up to two days for results, and a false negative rate as high as 1 in 5 mean alternative, large-scale COVID-19 screening tools are still being sought. SARS-CoV-2 is known to damage lung tissue, and in a distinct way that doctors are now seeking to exploit for new diagnostic approaches. Many COVID-19 patients develop pneumonia, which can progress to respiratory failure and sometimes death.

For patients with COVID-19, terrifying shortness of breath can set in virtually overnight. In many cases, it's caused by an aggressive pneumonia infection in the lungs, which fills them with thick fluid and robs the body of life-giving oxygen. Detecting these severe cases early on is essential for treating them successfully. At the moment, however, the only way to tell whether a patient's pneumonia is caused by the coronavirus is by examining X-ray and CT scans of the chest--and as cases rack up worldwide, radiologists are being deluged with images, creating a backlog that may delay critical decisions about care. One solution, said Karen Panetta, may involve taking some of that workload away from humans.

We've seen AI detect different cancers, kidney illness and brain tumors. Now, researchers from Mount Sinai believe they are the first in the US to use AI, combined with imaging and clinical data, to diagnose COVID-19. In a paper published in Nature Medicine today, they explain how they used CT scans of the chest -- along with symptoms, age, bloodwork and possible contact with the virus -- to spot the coronavirus disease. "We were able to show that the AI model was as accurate as an experienced radiologist in diagnosing the disease, and even better in some cases where there was no clear sign of lung disease on CT," said one of the lead authors, Zahi Fayad, director of the BioMedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine. The researchers note that scans don't always show lung diseases when a patient first presents symptoms and lab tests can take days to come back. The AI helps address both of those problems.

The new dataset contains more than 1,000 anonymised sets of chest CT scans. This expands on the earlier database of CT studies of patients with laboratory-confirmed infection created by scientists at the Diagnostics and Telemedicine Centre. The data set aims to inform AI to diagnose COVID-19. The dataset is the largest to date, and all CT studies in the dataset have a special marking made according to the classification, which reflects the manifestation of pathological abnormalities of COVID-19 in the lung tissue based on the chest computed tomography. According to experts at the Diagnostics and Telemedicine Center, a database with CT scans converted into the'research' Neuroimaging Informatics Technology Initiative (NIFTI) format is intended for developing artificial intelligence algorithms.

COVID-19 disease, caused by the SARS-CoV-2 virus, was identified in December 2019 in China and declared a global pandemic by the WHO on 11 March 2020. Artificial Intelligence (AI) is a potentially powerful tool in the fight against the COVID-19 pandemic. AI can, for present purposes, be defined as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions can be useful to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. Since the outbreak of the pandemic, there has been a scramble to use and explore AI, and other data analytic tools, for these purposes. In this article, I provide an early review, discussing the actual and potential contribution of AI to the fight against COVID-19, as well as the current constraints on these contributions. It aims to draw quick take-aways from a fast expanding discussion and growing body of work, in order to serve as an input for rapid responses in research, policy and medical analysis. The cost of the pandemic in terms of lives and economic damage will be terrible; at the time of writing, great uncertainty surrounded estimates of just how terrible, and of how successful both non-pharmaceutical and pharmaceutical responses can be. Improving AI, one of the most promising data analytic tools to have been developed over the past decade or so, so as to help reduce these uncertainties, is a worthwhile pursuit.

In the fight against COVID-19, several artificial intelligence labs are turning to an unexpected piece of evidence that might help diagnose the illness: people's voices. A team of researchers from Harvard and MIT is using machine learning to comb through voice recordings from COVID-19 patients and healthy people in an attempt to identify specific vocal signatures that could indicate someone is carrying the virus. A similar project is underway at Carnegie Mellon University's CyLab. Research is still in early stages, but the teams aim to develop AI tools that could tell people whether they have coronavirus based on an audio recording of their voice. If proven successful, the tools could allow more people to choose to self-isolate even if they don't have access to a COVID-19 test.

Artificial intelligence is here to save us from coronavirus. It spots new outbreaks, identifies people with fevers, diagnoses cases, prioritizes the patients most in need, reads the scientific literature, and is on its way to creating a cure. Alex Engler is a David M. Rubenstein Fellow at the Brookings Institution and an adjunct professor and affiliated scholar at Georgetown University's McCourt School of Public Policy. As the world confronts the outbreak of coronavirus, many have lauded AI as our omniscient secret weapon. Although corporate press releases and some media coverage sing its praises, AI will play only a marginal role in our fight against Covid-19.