Precision Medicine

Also called personalized medicine, this evolving field makes use of an individual’s genes, lifestyle, environment and other factors to identify unique disease risks and guide treatment decision-making.
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Medicare patients monitored by AI for risk factors requiring care

A health-management company in Arizona is expanding its use of AI to detect risk factors in Medicare Advantage patients by scanning doctors’ notes to automatically flag patients who need additional reimbursable care.

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Cancer overtakes heart disease as top killer in some countries

In the U.S., heart disease has been the reigning top cause of death for a number of years, but cancer is quickly becoming the top killer in some high-income and upper-middle-income countries, according to a new study published in The Lancet.

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AI with chaos-theory inputs could inform care for the developmentally disabled

Minor disruptions in routine can cause serious setbacks in individuals with learning challenges and other neurological disabilities. The time may be ripe for combining AI with chaos theory to predict effects and outcomes.

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AI gives OCT images a resolution boost

Duke University researchers have used AI to boost the resolution of optical coherence tomography (OCT) to improve medical images across fields, from cardiology to oncology. Their findings were recently published in nature photonics.

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FDA may use blockchain for medical recalls

Blockchain may be a future solution for the FDA to leverage medical recalls.

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HHS to fund Ebola vaccine

HHS will provide $23 million to drug manufacturer Merck to produce Ebola vaccine doses over the next year, the agency announced Aug. 21. 

How AI can be applied to an EKG to measure overall health

Overall health status may soon be measurable by applying AI to electrocardiogram data, according to a journal from the American Heart Association, Circulation: Arrhythmia and Electrophysiology.

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AI reduces diagnostic workload of detecting UTIs

Urinary tract infections make up a significant portion of microbiological screening in diagnostic laboratories, yet nearly two-thirds of samples come up negative. But AI has the potential to improve the process by reducing the number of query samples and enabling diagnostic services to concentrate on those that many have actual infections.