The tool is the outcome of a project named'Digital Control Centre for COVID-19' by health innovation body, EIT Health, which was initiated in April 2020. Since then, the tool has undergone development and validation, and has shown early success in the stratification and personalisation of treatment for patients with serious COVID-19, leading to improved treatment responses and a 50% reduction in mortality rates. The study has been published in Clinical Infectious Diseases. The main cause of death for patients with COVID-19 is respiratory failure, however, many of these patients can be effectively treated if adequate care is provided at the right timepoint. Researchers at Hospital Clinic Barcelona-IDIBAPS created the Artificial Intelligence solution capable of analysing, in real time, more than a trillion anonymised data points of COVID-19 patients, identifying clinical patterns and suggesting personalised treatments.
AI could also have a transformative effect on clinical decision-making through the utilisation of the huge levels of genomic, biomarker, phenotype, behavioural, biographical and clinical data that is generated across the health system. Bayer and Merck & Co provide a perfect example of this. They have developed an AI software system to support clinical decision-making of chronic thromboembolic pulmonary hypertension (CTEPH) – a rare form of pulmonary hypertension. The software helps differentiate patients from those suffering with similar symptoms that are actually a result of asthma and chronic obstructive pulmonary disease (COPD), and therefore diagnose CTEPH more reliably and efficiently. The CTEPH Pattern Recognition Artificial Intelligence obtained FDA Breakthrough Device Designation in December 2018.
This post provides an overview of chest CT scan machine learning organized by clinical goal, data representation, task, and model. A chest CT scan is a grayscale 3-dimensional medical image that depicts the chest, including the heart and lungs. CT scans are used for the diagnosis and monitoring of many different conditions including cancer, fractures, and infections. The clinical goal refers to the medical abnormality that is the focus of the study. Many CT machine learning papers focus on lung nodules.
In late February, a paper appeared in the journal Cell with encouraging news regarding one of the world's most persistent public health problems. Researchers at Massachusetts Institute of Technology and Harvard University had used artificial intelligence to identify a chemical compound with powerful antibiotic properties against some of the world's most drug-resistant strains of bacteria -- a welcome discovery in a world where 700,000 people die every year from drug-resistant infections. It was the first time an antibacterial compound had been identified this way. The researchers named it halicin, in honor of the computer HAL in the film 2001: Space Odyssey. While the global need for new antibiotics to treat drug-resistant infections is as pressing as it was at the start of the year, the world's attention has been diverted by the novel coronavirus pandemic, and the hunt for a vaccine that can halt Covid's spread.
Several deadly viral outbreaks have happened in every part of the world, making all the nations race for a vaccine development every time. Similarly, COVID-19 or the novel coronavirus demands numerous research teams to find a vaccine to fight against this lethal virus. Wonder how machine learning contributes? Advanced technology is the greatest strength that researchers have in this digital era as it gathers data from all resources and offers useful insights. It's not just the biological researchers who work for vaccine development.
Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.
We take a closer looking at some of the more unusual security research that was presented at this year's virtual Hacker Summer Camp The annual Hacker Summer Camp traversed from Las Vegas into the wilds of cyberspace this year, thanks to the coronavirus pandemic, but security researchers still rose to the challenge of maintaining the traditions of the event in 2020. As well as tackling core enterprise and web security threats, presenters at both Black Hat and DEF CON 2020 took hacking to weird and wonderful places. Anything with a computer inside was a target – a definition that these days includes cars, ATMs, medical devices, traffic lights, voting systems and much, much more. Security researcher Alan Michaels brought a new meaning to the phrase "insider threat" with a talk about the potential risk posed by implanted medical devices in secure spaces at Black Hat 2020. An aging national security workforce combined with the burgeoning, emerging market for medical devices means that the risk is far from theoretical.
Imagine a few days before an election, a video of a candidate is released, showing them using hate speech, racial slurs, and epithets that undercut their image as pro minorities. Imagine a teenager watching embarrassingly an explicit video of themselves going viral on social media. Imagine a CEO on the road to raise money when an audio clip stating her fears and anxieties about the product is sent to the investors, ruining her chances of success. All the above scenarios are fake, made up, and not actual, but can be made real by AI-generated synthetic media, also called deepfakes. The same technology that can enable a mother, losing her voice to Lou Gehrig's disease to talk to her family using a synthetic voice can also be used to generate a political candidate's fake speech to damage their reputation.
Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases. The COVID-19 pandemic has resulted in an accelerated development of applications for digital health, including symptom monitoring and contact tracing. Their potential is wide ranging and must be integrated into conventional approaches to public health for best effect.
Dallas-based Match operates several dating apps, including Tinder, Hinge and OkCupid, as well as its namesake brand. The company in July completed its separation from IAC/InterActiveCorp., which previously owned a roughly 80% stake. Match released video-chatting features for its apps in the spring as users started avoiding traditional dating spots such as bars and restaurants. The company is now in the beginning stages of developing features such as games and icebreakers to make those one-on-one video calls more engaging--part of a broader strategy to find new ways to generate revenue from its millions of users, according to Chief Financial Officer Gary Swidler. "We've got a lot of users, and I think there's more we can do with them," said Mr. Swidler, who is also Match's chief operating officer.