Artificial intelligence will offer ergonomic assessments to employers. Travelers Insurance Companies has started using artificial intelligence to administer ergonomic assessments, according to Insurance Journal. The company claims to be the first of its kind to provide this kind of service, which combines AI technology and ergonomic research to identify potential risk factors based on a video of an employee completing a task. The software produces a report based on the video, then an employee from Travelers develops an alternative, safer plan for the worker to follow. These ergonomic assessments are geared to enhance worker safety and reduce the number of workplace injuries.
We present a similar image retrieval (SIR) platform that is used to quickly discover visually similar products in a catalog of millions. Given the size, diversity, and dynamism of our catalog, product search poses many challenges. It can be addressed by building supervised models to tagging product images with labels representing themes and later retrieving them by labels. This approach suffices for common and perennial themes like "white shirt" or "lifestyle image of TV". It does not work for new themes such as "e-cigarettes", hard-to-define ones such as "image with a promotional badge", or the ones with short relevance span such as "Halloween costumes". SIR is ideal for such cases because it allows us to search by an example, not a pre-defined theme. We describe the steps - embedding computation, encoding, and indexing - that power the approximate nearest neighbor search back-end. We also highlight two applications of SIR. The first one is related to the detection of products with various types of potentially objectionable themes. This application is run with a sense of urgency, hence the typical time frame to train and bootstrap a model is not permitted. Also, these themes are often short-lived based on current trends, hence spending resources to build a lasting model is not justified. The second application is a variant item detection system where SIR helps discover visual variants that are hard to find through text search. We analyze the performance of SIR in the context of these applications.
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.
According to the World Health Organisation (WHO) [World Health Organization, 2013], the United Nations directing and coordinating health authority, public health surveillance is: The continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice. Public health surveillance practice has evolved over time. Although it was limited to pen and paper at the beginning of 20th century, it is now facilitated by huge advances in informatics. Information technology enhancements have changed the traditional approaches of capturing, storing, sharing and analysing of data and resulted efficient and reliable health surveillance techniques [Lombardo and Buckeridge, 2007]. The main objective and challenge of a health surveillance system is the earliest possible detection of a disease outbreak within a society for the purpose of protecting community health. In the past, before the widespread deployment of computers, health surveillance was based on reports received from medical care centres and laboratories.
This accessibility tech promises to make it safer than ever to live independently (Photo: Reviewed.com) Purchases you make through our links may earn us a commission. Technology may be entertaining, but at its essence, its primary function is to make our lives easier. When we want to find answers to our questions, communicate with friends, secure our homes, or hundreds of other scenarios, we turn to technology. At CES 2020, technology took on another role: helping us care for ourselves and loved ones.
Technology may be entertaining, but at its essence, its primary function is to make our lives easier. When we want to find answers to our questions, communicate with friends, secure our homes, or hundreds of other scenarios, we turn to technology. At CES 2020, technology took on another role: helping us care for ourselves and loved ones. In an effort to make living with disabilities and aging in place as safe and independent as possible, companies are promising smart technology that allows you to better assess you or a loved one's health and environment. Linksys Wellness Pods use WiFi to track motion and respiratory changes.
Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform ; email writing becomes much faster with machine learning (ML) based auto-completion ; many businesses have adopted natural language processing based chatbots as part of their customer services . AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports  to games such as poker  and Go . All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" . Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.
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 (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.
Artificial intelligence is revolutionising medicine – but can it do the same for occupational health? Occupational physician Dr Steve Boorman considers the opportunities and challenges of adopting AI systems in OH. Having spent the past few years of my career working in a technology company, I have seen at first hand the rapid developments in hardware and software that are changing the way we use and think about machines. Machine learning and artificial intelligence (AI) are now everyday realities and we would be foolish to think that occupational health (OH) practice will not see change driven by such technology. Gordon E Moore, the co-founder of technology company Intel, observed in 1965 that the number of transistors packed into a given amount of space would double every two years1.