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.
New data predicts the market for AI-driven healthcare technologies will exceed $6 billion in just three years. The surge is being driven largely by growing demand and acceptance among consumers for electronic, data-driven and virtual-based care, and the desire for more convenient, accessible, and affordable care. While it's entertaining to speculate on the future of these applications to healthcare, there are several use cases underway today which promise to change the way we think about and deliver care at the individual and population levels. These three areas highlight where AI is already making an impact in the delivery, treatment, and reimbursement of care. Tracking disease prevalence, treatment methods, and patient response through widespread systematic data collection, analysis, and dissemination has the potential to help us fine tune treatment protocols based on clear evidence of what's working and what's not across various disease states and populations.
The world's most powerful technology companies have a vision for the future of healthcare. You'll still go to your doctor's office, sit in a waiting room, and explain your problem to someone in a white coat. But instead of relying solely on their own experience and knowledge, your doctor will consult an algorithm that's been trained on the symptoms, diagnoses, and outcomes of millions of other patients. Instead of a radiologist reading your x-ray, a computer will be able to detect minute differences and instantly identify a tumor or lesion. AI systems like these, currently under development by companies including Google and IBM, can't read textbooks and journals, attend lectures, and do rounds--they need millions of real life examples to understand all the different variations between one patient and another.
Increased use of electronic medical records can improve treatments and diagnoses for patients, but they're also vulnerable to large data breaches. Are we sharing too much of our personal health data? It's a question worth asking after massive breaches of our personal health data in recent years and reports that, even in low-tech settings like a hospital waiting room, privacy protocols are faulty. According to the health trade publicationHIPAA Journal,more hospitals and doctors' practices reported breaches in 2016 than in any other year since the U.S. Department of Health and Human Services' Office of Civil Rights, which collects data on leaks, started publishing breach summaries in 2009. Among the latest leaks: Bronx-Lebanon Hospital Center in New York City left patients' names, home addresses, medical and mental health diagnoses, addiction histories, HIV statuses and even sexual assault and domestic violence reports exposed online.