The outbreak of the global pandemic has led to increased awareness and realization amongst the people about how technological advancements and innovations can be leveraged to help organizations and individuals to cope up with the drastic changes the world is witnessing across all levels. Consumers and their changing needs drive different industries. The way patients are consuming healthcare services is evolving, especially after the COVID-19, the consumer behavior has changed drastically. Pandemic, changing patients' needs and technological advancements are some of the few factors which are accelerating to change the dynamics of the healthcare industry. Patients are expecting new virtual and digital healthcare services from the healthcare institutions and to cater to those expectations, healthcare facilities have to change their work processes.
Artificial intelligence is a highly popular trend in the healthcare industry, especially the outbreak of the disastrous COVID-19 pandemic. AI in healthcare has generated some of the top machines to help to cure patients as well as boost productivity in hospitals. Robotics in healthcare provided robotic arms, and medical robots to help in surgeries and teach practical syllabus to residents. Artificial intelligence is set to take over the healthcare industry in the nearby tech-driven future. Governments of different countries have started allocating funds of millions of dollars for investing in applications of AI in healthcare.
Experiences with AI and machine learning at CVS Health and St. Luke's Health System in Boise, Idaho, are having practical benefits to the two organizations. CVS Health is learning how to scale AI applications using machine learning, especially through the house of machine learning operations (MLOps) tools, according to Nels Lindahl, director of Clinical Decision Systems, speaking in a virtual session at the recent Ai4 Conference held virtually recently. And St. Luke's Health Center put a COVID-19 prediction program, a supply chain purchase engine and a demand-based staffing application into initial production using AI and machine learning, said Dr. Justin Smith, senior director of advanced analytics at St. Luke's, also at a recent Ai4 virtual conference session. "We are at an MLOps tipping point, where ML has a growing production footprint, with adoption picking up pace and awareness and understanding at an all-time high," stated Lindahl. "ML tech can now deliver; people are seeing real use cases in the wild and having them grow; it's real."
The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026. Countries like the US spend a major chunk of their GDP on healthcare.
Artificial intelligence can enable busy NHS emergency departments to perform bedside checks for Covid-19 in just 10 minutes without the need for a laboratory, a study led by Oxford University shows. During a three-month evaluation at John Radcliffe Hospital, Oxford's main accident and emergency centre, the study found that AI test results were available 45 minutes after a patient arrived, 26% faster those for a lateral flow test. The AI screening test, known as CURIAL-Rapide, uses routine healthcare data (blood tests and vital signs) to screen patients for Covid-19. Compared to lateral flow tests, the AI test was more likely to identify Covid-19 in patients and correctly ruled out the infection 99.7% of the time, the research found. In addition, a collaboration with five NHS trusts between December 2020 and March 2021 – University Hospitals Birmingham, Portsmouth University and Bedfordshire Hospitals – the study found that the AI test performed consistently in 72,000 admissions. It provided reliable negative results for uninfected patients up to 98.8% of the time and was 21% more effective at identifying Covid-19 positive patients than lateral flow tests.
Bommasani, Rishi, Hudson, Drew A., Adeli, Ehsan, Altman, Russ, Arora, Simran, von Arx, Sydney, Bernstein, Michael S., Bohg, Jeannette, Bosselut, Antoine, Brunskill, Emma, Brynjolfsson, Erik, Buch, Shyamal, Card, Dallas, Castellon, Rodrigo, Chatterji, Niladri, Chen, Annie, Creel, Kathleen, Davis, Jared Quincy, Demszky, Dora, Donahue, Chris, Doumbouya, Moussa, Durmus, Esin, Ermon, Stefano, Etchemendy, John, Ethayarajh, Kawin, Fei-Fei, Li, Finn, Chelsea, Gale, Trevor, Gillespie, Lauren, Goel, Karan, Goodman, Noah, Grossman, Shelby, Guha, Neel, Hashimoto, Tatsunori, Henderson, Peter, Hewitt, John, Ho, Daniel E., Hong, Jenny, Hsu, Kyle, Huang, Jing, Icard, Thomas, Jain, Saahil, Jurafsky, Dan, Kalluri, Pratyusha, Karamcheti, Siddharth, Keeling, Geoff, Khani, Fereshte, Khattab, Omar, Kohd, Pang Wei, Krass, Mark, Krishna, Ranjay, Kuditipudi, Rohith, Kumar, Ananya, Ladhak, Faisal, Lee, Mina, Lee, Tony, Leskovec, Jure, Levent, Isabelle, Li, Xiang Lisa, Li, Xuechen, Ma, Tengyu, Malik, Ali, Manning, Christopher D., Mirchandani, Suvir, Mitchell, Eric, Munyikwa, Zanele, Nair, Suraj, Narayan, Avanika, Narayanan, Deepak, Newman, Ben, Nie, Allen, Niebles, Juan Carlos, Nilforoshan, Hamed, Nyarko, Julian, Ogut, Giray, Orr, Laurel, Papadimitriou, Isabel, Park, Joon Sung, Piech, Chris, Portelance, Eva, Potts, Christopher, Raghunathan, Aditi, Reich, Rob, Ren, Hongyu, Rong, Frieda, Roohani, Yusuf, Ruiz, Camilo, Ryan, Jack, Ré, Christopher, Sadigh, Dorsa, Sagawa, Shiori, Santhanam, Keshav, Shih, Andy, Srinivasan, Krishnan, Tamkin, Alex, Taori, Rohan, Thomas, Armin W., Tramèr, Florian, Wang, Rose E., Wang, William, Wu, Bohan, Wu, Jiajun, Wu, Yuhuai, Xie, Sang Michael, Yasunaga, Michihiro, You, Jiaxuan, Zaharia, Matei, Zhang, Michael, Zhang, Tianyi, Zhang, Xikun, Zhang, Yuhui, Zheng, Lucia, Zhou, Kaitlyn, Liang, Percy
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.
Right about now, a whole lot of parents are looking around and asking themselves: What is school going to look like this year? Here in New York, this is the time of year when I get letters telling me who my kids' teachers are going to be and how to track down school supplies. In other parts of the country, kids are already back in classrooms. And after more than a year of disrupted and hybrid learning, everyone has had this hope that this year will be different. You just have to press play on a couple of videos from school board meetings across the country to realize how elusive "normal" still is. A lot of the meetings I've been watching recently are about masks--who should be wearing them and who shouldn't.
ADATA Technology has collaborated with researchers at Hsinchu National Taiwan University Hospital (NTUH) to introduce the C-Rob Autonomous Mobile Robots. These robots use Artificial Intelligence (AI) to reduce the workload of healthcare workers as Taiwan continues to combat the Covid-19 pandemic. Recently, an outbreak of Covid-19 struck Taiwan, and hospitals are prone to becoming hotspots for transmission. When Covid-infected patients enter hospitals, whether for testing or much-needed medical care, hospital staff will often prioritize these patients and devote less time to those visiting the hospital for non-Covid related reasons. On top of this, a clean environment must be maintained, with frequent disinfection to reduce the risk of transmission.
The COVID-19 pandemic has created numerous challenges in healthcare, but challenges can sometimes breed innovation. Technological innovation in particular is poised to change the way care is delivered, driving efficiency in the process. Efficiency will be key as hospitals and health systems look to recover from the initial, devastating wave of the pandemic. Ryan Hodgin, chief technology officer for IBM Global Healthcare, and Kate Huey, partner at IBM Healthcare, will speak about some of these technological innovations in their digital HIMSS21 session, "Innovation Driven Resiliency: Redefining What's Possible." The technology in question can encompass telehealth, artificial intelligence, automation, blockchain, chatbots, apps and other elements that have become mainstays of healthcare during the course of the pandemic.
The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multi-agent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.