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AI just at the early stages of showing real results


Artificial intelligence--a broad set of technologies that enable machines to mimic the human brain's ability to process information, learn and adapt--holds potential in healthcare to improve patient outcomes and reduce costs, but it hasn't yet been widely adopted in daily clinical practice.

What's Next in CT Technology


Systems continue to evolve and expand in ways that benefit radiologists, providers, and patients. CT imaging in the emergency department (ED) is increasing rapidly. In fact, it now comprises more than 35% of all CT procedures in the United States. Today's CT scanners include technological developments that enable customers to better manage patient care, including lung cancer screening, dose guidance and regulation, spectral and multienergy imaging, and expansion of cardiac and brain imaging. These scanners and solutions also provide new levels of information to help clinicians make a more confident diagnosis at low dose, without increasing complexity in their routines.

Artificial Intelligence all set to Virtualize Radiology Brains


Accelerating with an exponential growth, artificial intelligence (AI) is all set to move from experimental stages to live industry implementations and all is set to mark its presence across all industry verticals. AI is all about virtualizing human cognitive functions in the form of software brains. For organizations, harnessing AI is not optional, albeit it is critical to stay competitive. Gartner in its recent study (2018), predicts the business value derived from AI to reach $3.9 trillion by 2022. With the disruptive potential, the investments in AI are ever-increasing.

The role of AI in Healthcare – an in-depth guide


Is Artificial Intelligence (AI) the silver bullet that will make doctors all over the world unemployed? Will AI be able to outperform oncologist in creating treatment plans for cancer patients? Keep reading and get new perspectives on healthcare AI as I untangle opportunities and grand challenges within the field. "Too much information, too little time" is one of the big challenges in healthcare today. Patients, healthcare professionals and medical devices generate huge amounts of data.

Economics of AI, Design Thinking, and Data Science for Smart Healthcare


Health systems are multi- faceted and continually changing across a variety of contexts and health service levels. For example one of the critical challenges of the resource deficient public health infrastructures worldwide is the spread of the communicable diseases. As seen during the outbreaks of the fatal communicable diseases like Severe Acute Respiratory Syndrome (SARS) in 2003, H1N1 in 2009, the Zika virus in 2016, Ebola and Middle East respiratory syndrome (MERS) in 2014, and the Nipah virus in 2018, infectious diseases can spread rapidly within the countries as well as across the national borders. Artificial intelligence (AI) has been making its way into the healthcare sector, presenting a variety of possibilities in disease diagnosis, treatment, and prevention. The adoption of artificial intelligence in the healthcare sector is growing substantially.