Expanded partnerships, access to clinical trials, and new medical and behavioral treatments and interventions reaching individuals more quickly will benefit communities in Pennsylvania and beyond thanks to the renewal of Penn State's Clinical and Translational Science Award (CTSA) funded by the National Institutes of Health (NIH). The NIH's National Center for Advancing Translational Sciences (NCATS) awarded Penn State more than $25 million to provide critical clinical and translational research infrastructure and continue building collaborations across the University's campuses and with communities around the state. NCATS' CTSA Program develops innovative solutions to improve processes for turning laboratory, clinical and community research into health knowledge, interventions and treatments. CTSA institutions partner to advance biomedical and health research and share best practices and tools. Penn State is one of 64 funded CTSA organizations nationally and is among the few that serve primarily rural communities.

Nvidia is launching the $100 million Cambridge-1, the most powerful supercomputer in the United Kingdom, and it is making it available to external researchers in the U.K. health care industry. The machine will be used for AI research in health care, and it's one of the world's fastest supercomputers. Nvidia will make it available to accelerate research in digital biology, genomics, and quantum computing. Nvidia is collaborating with AstraZeneca, maker of one of the COVID-19 vaccines, to fuel faster drug discoveries and creating a transformer-based generative AI model for chemical structures. Transformer-based neural network architectures, which have become available only in the last several years, allow researchers to leverage massive datasets using self-supervised training methods, avoiding the need for manually labeled examples during pre-training.

This article is part 1 of our bias in AI series, an update to the original article in our Biotech Review of the year – issue 8. Read part 2 here. During the COVID-19 pandemic, the notion of different health outcomes for different populations has gained increased profile in the public consciousness, particularly in light of the varying effect of COVID-19 on different community groups. Varying outcomes can arise for a variety of reasons, one of which is bias (whether conscious or unconscious) in the healthcare system. But surely this isn't something that needs to be considered in relation to AI in health research, as AI systems are inanimate and can't display human faults…right? There is often a misconception that medical devices and AI systems can't produce biased results, as they work using logic and process, rather than being tainted by flawed assumptions based on human error or prejudice. However, ultimately it is humans that design medical devices, which are tested on human collected datasets.

Machine learning for health must be reproducible to ensure reliable clinical use. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. We propose recommendations to address this problem.

NHS patients will benefit from new artificial intelligence (AI) technologies thanks to a £50 million boost. A range of AI-powered innovations which can analyse breast cancer screening scans and assess emergency stroke patients will be tested and scaled. Take-home technology could also see patients given devices and software that can turn their smartphone into a clinical grade medical device for monitoring kidney disease, or a wearable patch to detect irregular heartbeats, one of the leading causes of strokes and heart attacks. The award is managed by the Accelerated Access Collaborative in partnership with NHSX and the National Institute for Health Research. The package also includes funding to support the research, development and testing of promising ideas which could be used in the NHS in future to help speed up diagnosis or improve care for a range of conditions including sepsis, cancer and Parkinson's.

Rational intelligence – the exclusively human capacity of thinking, speculating, knowing – has been, since the start of the Modern Era, the keystone of our place in the world: "More than nature, different from machines". While we have intelligence, animals only have instincts and machines are simply mechanical. But today, these principles are being questioned. For one thing, the climatic and social emergency obliges us to put the superiority of rational intelligence on stand-by, at least as regards conservation and continuation of life. For another thing, artificial intelligence is better than human beings at resolving certain "rational" problems, leading us to wonder whether this is the relevant difference.

Dr. Alan Bernstein is president and CEO of CIFAR and was the founding president of the Canadian Institutes of Health Research (CIHR) during the SARS epidemic As president of the Canadian Institutes of Health Research (CIHR) in 2003 when an earlier coronavirus, SARS-CoV-1, swept through Canada, I had a unique vantage point from which to view Canada's response to an unprecedented threat to public health. Today, as I watch Canada's response to COVID-19, it's striking to me how much we've learned about the science and policies needed to address such crises. By and large, Canadians trust their governments, and our ministers are responding admirably to the crisis. I've been impressed with the consistent and clear communication, the co-ordination with the provinces and the rapid implementation of very significant financial packages aimed at dealing with the economic, social and health consequences of the pandemic. I'm proud that Canada has a socially cohesive society in which we place a high value on community well-being.

In recent years, artificial intelligence (AI) has become a hot topic, largely due to its potential to transform the ability of computers to solve increasingly complex problems in technology and society. Machines that are able to learn and "think" like a human brain offer great potential in advancing science and innovation by evaluating complicated scenarios in a fraction of the time it would take a person. While AI is still in its early stages, the biotech industry is already leveraging AI tools to accelerate drug discovery and advance health research. Worldwide, health research is occurring at a larger volume than any time in human history. Thousands of peer-reviewed articles are generated every month – a single person cannot keep up with the constant surge of new information, let alone quickly process and integrate it into their existing knowledge base.

Machines that are able to learn and "think" like a human brain offer great potential in advancing science and innovation by evaluating complicated scenarios in a fraction of the time it would take a person. While AI is still in its early stages, the biotech industry is already leveraging AI tools to accelerate drug discovery and advance health research. Worldwide, health research is occurring at a larger volume than any time in human history. Thousands of peer-reviewed articles are generated every month – a single person cannot keep up with the constant surge of new information, let alone quickly process and integrate it into their existing knowledge base. Many biotech researchers are now using AI to manage the onslaught of data and make sure no meaningful pieces slip through the cracks.

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast "clinical research" versus routine "clinical care," outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures ...