Collaborating Authors


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

Inside Google's Quest for Millions of Medical Records


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 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


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 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.

Identifying and working with sensitive healthcare data with Amazon Comprehend Medical Amazon Web Services


SUBJECTIVE: Carlos came to the ED via ambulance accompanied by son, Jorge. He is a 50 yo male who was working at Food Corp when he had sudden onset of palpitations. Carlos stated his fater, Diego, also had palpitations through his life.

Advancement of AI Opens Health Data Privacy to Attack


Advances in artificial intelligence have created new threats to the privacy of health data, according to a new study by University of California, Berkeley researchers. University of California, Berkeley (UC Berkeley) researchers have found that artificial intelligence (AI) innovations have created new threats to health data privacy against which current laws and regulations cannot adequately safeguard. The researchers demonstrated that AI can be used to identify individuals by learning daily patterns in step data--like that collected by activity trackers, smartwatches, and smartphones--and correlating it to demographic data. Said UC Berkeley's Anil Aswani, "In principle, you could imagine Facebook gathering step data from the app on your smartphone, then buying healthcare data from another company and matching the two. Now they would have healthcare data that's matched to names, and they could either start selling advertising based on that or they could sell the data to others."