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Blockchain for Increased Trust in Virtual Health Care: Proof-of-Concept Study


Background: Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today's health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments. Objective: This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements. Methods: This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering. Results: This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed. Conclusions: A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0. As a result of health care development, societies are undergoing a current demographic shift--people live longer, and fewer are born. The overall increase in life expectancy between 1970 and 2013 was 10.4 years on average for Organization for Economic Cooperation and Development countries [1].

Artificial Intelligence in Chemistry: Current Trends and Future Directions


The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications.

The best consultancy for business, with sales and marketing data insights too: we review


With digital marketing, good, clean, and insightful data is a key pillar which a business stands to drive growth and profits. Having clear and precise data-driven outcomes should be a priority for all marketers. When used in tandem with well-defined marketing and sales goals, and various marketing tools and techniques, companies will discover that their lead to sale conversion process can be far less cumbersome and more rewarding. Possessing clean data will help marketers identify detailed segments based on user attributes, past behaviours, interactions, and other necessary data points. Data can be leveraged for highly targeted campaigns which will drive marketing return on investment (ROI).

Ethical AI will not see broad adoption by 2030, study suggests


All the sessions from Transform 2021 are available on-demand now. According to a new report released by the Pew Research Center and Elon University's Imaging the Internet Center, experts doubt that ethical AI design will be broadly adopted within the next decade. In a survey of 602 technology innovators, business and policy leaders, researchers, and activists, a majority worried that the evolution of AI by 2030 will continue to be primarily focused on optimizing profits and social control and that stakeholders will struggle to achieve a consensus about ethics. Implementing AI ethically means different things to different companies. For some, "ethical" implies adopting AI -- which people are naturally inclined to trust even when it's malicious -- in a manner that's transparent, responsible, and accountable. For others, it means ensuring that their use of AI remains consistent with laws, regulations, norms, customer expectations, and organizational values.

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches


COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis.

A Primer of Neural Networks


With the advancement of technology, Artificial Intelligence starts to live its golden age. We wake up everyday to new and exciting inventions that can be used for the benefit of living things. Throughout the history, human beings are influenced by the nature. We use nature to cope with the problems we encountered by mimicking it. A lot of tools and vehicles are inspired by animals and nature.

Council Post: An AI Road Map Starts With Data


CTO of Infostretch, a digital engineering services company that helps enterprises prosper in the digital age. Several years into what many people expected to be an AI revolution, there is a nagging sense that we are at a crossroads. Artificial intelligence is an evolutionary step forward for business optimization strategies -- and rightly so -- but the companies that saw AI as the path to the promised land could be forgiven for thinking that the hype has outweighed successful implementation. Granted, there are numerous organizations that have integrated AI into their business processes, and it is already a routine part of software development, cybersecurity, natural language processing and robotic process automation (RPA). And yes, making AI a priority in terms of scalability and an accelerated time to market has shown a modicum of success.

Optimal Auctions Through Deep Learning

Communications of the ACM

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30–40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multilayer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown. Optimal auction design is one of the cornerstones of economic theory. It is of great practical importance, as auctions are used across industries and by the public sector to organize the sale of their products and services. Concrete examples are the US FCC Incentive Auction, the sponsored search auctions conducted by web search engines such as Google, and the auctions run on platforms such as eBay. In the standard independent private valuations model, each bidder has a valuation function over subsets of items, drawn independently from not necessarily identical distributions.

Best practices to build data literacy into your Gen Z workforce - Data Dreamer


This is a guest post by Kirk Borne, Ph.D., Chief Science Officer at, Kirk is also a consultant, astrophysicist, data scientist, blogger, data literacy advocate and renowned speaker, and is one of the most recognized names in the industry. A survey of 1,100 data practitioners and business leaders reported that 84% of organizations consider data literacy to be a core business skill, agreeing with the statement that the inability of the workforce to use and analyze data effectively can hamper their business success. In addition, 36% said data literacy is crucial to future-proofing their business. Another survey found that 75% of employees are not comfortable using data.

A Comprehensive Survey of Existing Chatbot Architectures and Techniques(Part-1)


In this series of blogs, I will try to investigate various types of dialogue systems or more commonly knows as chatbots that exist and what are some of the design techniques and algorithms used to develop these systems. Since their inception in the 1960s dialogue systems have gained increasing attention due to their ability to streamline conversations between humans and machines. User experience and involvement have become an important factor for the growth of businesses across the globe and dialogue systems are a perfect way to engage a user to enhance their overall experience. Taking this into account this blog will cover the various chatbot architectures ranging from rule-based to generation-based. I will further investigate the social responsiveness of these dialogue systems and how we can achieve the architecture for the implementation of these chatbots in the next blog.