Tag Archives: technology

A Pictorial Essay of Immunotherapy: Complications that Internists Will See, Whether They Like it or Not

Delirium is highly prevalent in hospitalized patients and is a strong and consistent negative predictor of length of stay, mortality, and long-term cognitive outcomes.1 Symptoms commonly associated with delirium include reduced ability to focus, sleep disturbances, psychomotor agitation, and emotional disturbances. The management of the behavioral disturbances of delirium is challenging. Although non-pharmacologic means to reduce […]

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doctor in uniform using futuristic looking digital screens and keyboard

Artificial Intelligence Transforms the Future of Health Care

Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from […]

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Swelling at the periorbital area (A). Extraocular muscle swelling (arrow head) in computed tomography (CT) (B). Positron emission tomography (PET) showed increased 18-fluorodeoxyglucose uptake (arrow head) at orbital (C) and hilar area (D).

Swollen Extraocular Muscle and Tingling Extremities

A 55-year-old man with a 3-month history of numbness and tingling in his extremities presented at a hospital. After obtaining a diagnosis of seronegative arthritis, the patient underwent immunosuppressive therapy with prednisone (5 mg/day) and salazosulfapyridine (100 mg/day). However, the symptoms did not improve, and he later experienced double vision. After 10 days, the patient […]

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What Happens When an Unstoppable Force Meets an Immovable Object?
This paradox plays out in many high-stakes global arenas—geopolitics, climate change, and financial markets, to name a few. Artificial intelligence thrives on massive disparate data sets, so it is not surprising that these data-dense megatrends are being shaped by artificial intelligence1 Although most humans do not face election, live near receding glaciers, or trade in cryptocurrencies, we are all learning that when manmade forces and intelligent machines collide, the costs and consequences can be real.

So it is with human health data.

Humans share 1 inherent right and bear 1 attendant risk—good health. In an inexorably older and progressively sicker world,2 another potential cataclysm confronts patients and physicians—the silent explosion of health data. The uses and fates of these health data, which will have grown from an estimated 153 exabytes in 2013 to 2314 exabytes by 2020 in the United States alone, are already being influenced by artificial intelligence.3 Medical students and trainees learn humanistic principles based on the Hippocratic precept, Primum non nocere (‘First do no harm’). Will humans use intelligent machines wisely, using big data responsibly for health care and medical training? Or will their power and immensity paradoxically produce unintended harms?

The expanding digital health data universe is the unstoppable force saturating the Cloud with big data droplets.

Individual personal health information resides in and fluxes through 2 types of data repositories. Administrative health care databases are operated by entities responsible for resourcing care and managing costs in socialized and quasi-market health insurance systems. Administrative health care databases are massive multigenerational payloads of demographic and utilization data (ie, pharmacy, physician, ambulatory, and hospital services)4 linked to multiple electronic medical records (EMRs) and increasingly homed in Cloud platforms compliant with the Health Insurance Portability and Accountability Act. Despite informatician and analyst expertise, their complexity and the density of their data defy standard statistical methods, limiting their applications to health care process and utilization management.

 CLEF = Cross Language Evaluation Forum; CNN = convolutional neural network; LSTM = long short-term memory unit; METEOR = Metric for Evaluation of Translation with Explicit Ordering; MS COCO = Microsoft Coco framework; RNN = recurrent neural network.

The Big Health Data–Intelligent Machine Paradox

What Happens When an Unstoppable Force Meets an Immovable Object? This paradox plays out in many high-stakes global arenas—geopolitics, climate change, and financial markets, to name a few. Artificial intelligence thrives on massive disparate data sets, so it is not surprising that these data-dense megatrends are being shaped by artificial intelligence1 Although most humans do not […]

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doctor in uniform using futuristic looking digital screens and keyboard

Sherlock Holmes and the Case of the Vanishing Examination

We live in a world steeped in technology where we all spend increasing amounts of time on our cell phones and computers and less time observing the world around us. These changes have also affected how we practice medicine. Many physicians today spend more time reviewing imaging studies and laboratory tests and less time taking […]

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Accuracy and Efficacy of Hand-Held Echocardiography in Diagnosing Valve Disease: A Systematic Review

In recent years, advances in technology have enabled hand-held echocardiography (HHE) to generate high-quality 2-dimensional and color Doppler images. As these devices become smaller, simpler, and more affordable, the question of whether HHE can augment or replace auscultation as the primary mode of cardiovascular diagnosis has become increasingly more relevant. If widely implemented, HHE has […]

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