AI can improve populations' lives by providing better services in the following aspects: The COVID-19 crisis has sped up the adoption of artificial intelligence in the sector. With the on-going pandemic, governments are rethinking and reconfiguring their business models to navigate the uncertainties of the post COVID-19 world, they have started realising the potential of artificial intelligence to increase resilience, spot growth opportunities and drive innovation. Taken together, these benefits would equip public sector organizations to move beyond process optimization to deliver world class services and tackle long-term global challenges. Governments face particular barriers to deploying AI on a bigger scale. Not surprisingly, the historically low levels of IT investment in the public sector have slowed the introduction of AI in the public sector.
Another AI-based living assistant provides pregnant women with guidance at various stages of pregnancy. The device acts as a communication platform for all of the people concerned and offers AI-informed advice. Such technology can raise pregnant women's awareness of the need to improve their self-care, especially in rural and remote areas where access to doctors and hospitals may be more limited. AI has also recently been recognized as one of the most accurate and reliable prediction systems. Health professionals can employ AI to precisely diagnose, manage, and predict different types of diseases at an early stage and estimate the patient's survival rate.
Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.
Google and Microsoft, rivals in cloud computing, have turned their attention to healthcare as they look to win over hospital systems as customers. Google has struck long-running partnerships with insurer Highmark, where it plans to build tools to help patients to share health information between visits, and Mayo Clinic, where it is tasked with developing a suite of AI solutions. Microsoft, in the meantime, made one of its largest acquisitions to date, with its $19.7 billion purchase of clinical documentation company Nuance. At MedCity INVEST Digital Health, leaders from both companies talked about their interoperability efforts and the future of AI in healthcare. Both rivals agreed on one thing: that these solutions should serve as a support, not a replacement, for clinicians.
Image Segmentation is considered a vital task in Computer Vision – along with Object Detection – as it involves understanding what is given in the image at a pixel level. It provides a comprehensive description that includes the information of the object, category, position, and shape of the given image. There are various algorithms for Image Segmentation that have been developed with applications such as scene understanding, medical image analysis, robotics, augmented reality, video surveillance, etc. The advent of Deep Learning in Computer Vision has diversified the capabilities of the existing algorithms and paved the way for new algorithms for pixel-level labeling problems such as Semantic Segmentation. These algorithms learn rich representations for the problem, including automatic pixel labeling of images in an end-to-end fashion.
These measures demonstrate the UK's commitment, following our exit from the European Union, to drive innovation in healthcare and improve patient outcomes. The exciting and fast developing field of software and artificial intelligence (AI) as a medical device has an increasingly prominent role within health systems. Applications of AI to be regulated as medical devices can range from screening, to diagnosis, to treatment, and to management of chronic conditions. Regulatory measures will be updated to further protect patient safety and take account of these technological advances. The MHRA has developed an extensive work programme to inform regulatory changes including key reforms across the software as a medical device lifecycle, from qualification to classification, to requirements that apply pre and post-market.
The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant.
Scientists have successfully used artificial intelligence to create a new drug regime for children with a deadly form of brain cancer that has not seen survival rates improve for more than half a century. The breakthrough, revealed in the journal Cancer Discovery, is set to usher in an "exciting" new era where AI can be harnessed to invent and develop new treatments for all types of cancer, experts say. "The use of AI promises to have a transformative effect on drug discovery," said Prof Kristian Helin, chief executive of The Institute of Cancer Research (ICR), London, where a team of scientists, doctors and data analysts made the discovery. "In this study, use of AI has identified a drug combination which appears to have promise as a future treatment for some children with incurable brain cancer. It's exciting to think that it could become one of the first examples of a treatment proposed by AI going on to benefit patients."
The FDA has authorized the first artificial intelligence software to help doctors detect prostate cancer. The program, called Paige Prostate, is the first approved AI system in pathology. "We really believe this product can make a huge difference," Paige CEO Leo Grady, PhD, says. The program was approved to help doctors, not to replace them. "For a second opinion today, you ship a glass slide to somebody else or you do another stain that's really expensive or you do another molecular test," Grady says.