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AI and IoT Power Self-Serve Health Clinics

#artificialintelligence

Advances in China's standard of living provide more people with access to healthcare. Nonetheless, with life expectancies now averaging 76.5 years, medical costs are on the rise. And while the number of top-tier hospitals throughout the country has more than doubled, the annual number of outpatient visits increased almost fourfold during that same period. Improving patient outcomes now relies on the use of new technologies such as real-time analytics, facial recognition, and the IoT. Innovation enables more people to get better access to healthcare information and advice without going to a hospital or waiting to see a doctor. It can also reduce the strain on overburdened medical personnel and resources by automating collection, transmission, and storage of healthcare data used in patient records.


Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy

arXiv.org Artificial Intelligence

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.


Secure and Robust Machine Learning for Healthcare: A Survey

arXiv.org Machine Learning

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.


Inside Google's Quest for Millions of Medical Records

#artificialintelligence

Cerner was interviewing Silicon Valley giants to pick a storage provider for 250 million health records, one of the largest collections of U.S. patient data. Google dispatched former chief executive Eric Schmidt to personally pitch Cerner over several phone calls and offered around $250 million in discounts and incentives, people familiar with the matter say. Google had a bigger goal in pushing for the deal than dollars and cents: a way to expand its effort to collect, analyze and aggregate health data on millions of Americans. Google representatives were vague in answering questions about how Cerner's data would be used, making the health-care company's executives wary, the people say. Eventually, Cerner struck a storage deal with Amazon.com The failed Cerner deal reveals an emerging challenge to Google's move into health care: gaining the trust of health care partners and the public.


The Double-edged Sword of AI and Machine Learning on Healthcare Data Security

#artificialintelligence

The UAE government is leading the way in establishing the necessary integrated and secure data ecosystem to expedite the implementation of future technologies like Artificial Intelligence (AI) in healthcare, which use data from many disparate sources to produce unprecedented services that will transform all aspects of people's wellness and everyday life. AI and machine learning offers hope in reducing the risk and impact of cyber-attacks on patient data, but also opens doors to potential wrong doers – "The Bad Guys" – by its very nature. Security threats are, and always have been, major concerns to healthcare organizations due to the value and vulnerability of the clinical data that is being recorded and distributed. The value of the data comes from the fact that it directly affects our ability to safely treat patients. Due to its content and historical nature it can be very big, so it takes a long time to rebuild, and it contains more than just clinical data.


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.


Tracking your pregnancy on an app may be more public than you think

Washington Post - Technology News

Like millions of women, Diana Diller was a devoted user of the pregnancy-tracking app Ovia, logging in every night to record new details on a screen asking about her bodily functions, sex drive, medications and mood. When she gave birth last spring, she used the app to chart her baby's first online medical data -- including her name, her location and whether there had been any complications -- before leaving the hospital's recovery room. But someone else was regularly checking in, too: her employer, which paid to gain access to the intimate details of its workers' personal lives, from their trying-to-conceive months to early motherhood. Diller's bosses could look up aggregate data on how many workers using Ovia's fertility, pregnancy and parenting apps had faced high-risk pregnancies or gave birth prematurely; the top medical questions they had researched; and how soon the new moms planned to return to work. "Maybe I'm naive, but I thought of it as positive reinforcement: They're trying to help me take care of myself," said Diller, 39, an event planner in Los Angeles for the video game company Activision Blizzard.


How is data reinventing the way we are addressing global healthcare challenges?

#artificialintelligence

Access to reliable information and connectivity in the healthcare ecosystem are the crucial factors in modernising health, care and prevention delivery. To tackle global health risks and rising healthcare costs, to effectively fight health inequalities and strengthen medical research, public health is reaching for technology. "There are, in effect, two things, to know and to believe one knows; to know is science; to believe one knows is ignorance," said Hippocrates. Today's global health strategies, policies, and decisions still too often hinge on conjectures and incomplete information. The OECD report "Health at a Glance: Europe 2018" suggests that up to one-fifth of health spending is wasteful and could be eliminated without undermining health system performance.


Big Data Meet Cyber-Physical Systems: A Panoramic Survey

arXiv.org Machine Learning

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.


Four trends powering healthcare AI, and what you can do about it

#artificialintelligence

Healthcare is drowning in data. An article by CIO.com outlines How CIOs Can Prepare for Healthcare'Data Tsunami.' Digitization of healthcare information, EHR systems, precision and personalized medicine, health information exchange, consumer health, Internet of Medical Things (IoMT), and other major trends affecting healthcare are accelerating this data growth rate. Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools that empower healthcare organizations to process the tsunami of healthcare data in near real time, maximize the value of this data, and delivering actionable insights near real-time insights that in turn enable healthcare to maximize the Quadruple Aim Objectives of improving patient outcomes, reducing healthcare costs, and improving the experiences of both patients and healthcare professionals. Increasingly healthcare organizations run AI / ML workloads in the cloud to reduce costs, and improve security, agility, and scalability. Clinician burnout is a major concern across healthcare providers with 42 percent of physicians indicating burnout in a recent research study by Medscape, and their job is the major lifestyle factor, with too many bureaucratic tasks (for example charting, paperwork) cited as the major task factor.