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ARTIFICIAL INTELLIGENCE, A TRANSFORMATIONAL FORCE FOR THE HEALTHCARE INDUSTRY

#artificialintelligence

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.


Artificial Intelligence, a Transformational Force for the Healthcare Industry

#artificialintelligence

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.


Large expert-curated database for benchmarking document similarity detection in biomedical literature search

#artificialintelligence

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.


Precision Medicine Informatics: Principles, Prospects, and Challenges

arXiv.org Artificial Intelligence

Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.


How Health Care AI Systems Are Changing Care Delivery - NEJM Catalyst

#artificialintelligence

A nurse avatar named "Molly" who regularly talks with patients about their symptoms and medical needs. Voice-recognition software that helps physicians document clinical encounters. A prescription drug-monitoring platform that can detect patients' opioid misuse. Systems that analyze millions of medical images to help physicians diagnose and predict diseases. Robots that extend the reach of surgeons.


Knowledge-based Biomedical Data Science 2019

arXiv.org Artificial Intelligence

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.




AI could help reduce the administrative costs of health care

#artificialintelligence

It's no secret that the U.S. spends a lot on health care, around 18 percent of its GDP or $9,400 per capita, nearly double what other high-income countries such as Canada, UK, Germany, and Australia spend. But more spending doesn't necessarily yield better results. In fact, studies show that many of the countries that spend less than the U.S. see better outcomes in the overall health of their citizens. According to a new report published by the Journal of the American Medical Association (JAMA), a little less than half the health care expenditures in the U.S. go into planning, regulating, and managing medical services at the administrative level. And industry experts believe we can reduce a lot of this spending with the help of artificial intelligence.