This article originally appeared on www.healthfurther.com.
Artificial intelligence is not to be feared by healthcare executives, but embraced.
With the majority of U.S. hospitals set to implement artificial intelligence over the next 10 years, it’s no surprise that the healthcare industry is about to undergo a massive shift in how patient care and effective hospital administration are delivered.
Yet, despite its potential to radically improve disease treatment and prevention, reduce administrative burden, and strengthen data security, uncertainty remains on how artificial intelligence will (or will not) transform the industry.
Presently, half of U.S. hospital executives are investing in new AI applications, which are already saving hospitals millions of dollars every year.
We’re at an exciting point in the application of these emerging technologies where there’s enough proven technology currently on the market to confidently invest in, with the potential has only having begun to be realized.
In this article, we take a look at some of the most exciting ways AI promises to reshape the healthcare industry, including:
- The ability to automatically monitor patients in intensive care with artificially intelligent driven analytic tools that predict complications early.
- Automating patient outreach with chatbots that take over repetitive administrative tasks, freeing staff to work on value-generating projects.
- Improving hospital efficiency with AI command centers that leverage the massive amounts of data generated by hospitals to increase operational efficiency.
- Remotely monitoring chronic conditions with smartphone apps and other wearable devices that use AI to help monitor patients health.
- Enhancing robotic surgery and improving surgical outcomes with increased accuracy, safety and efficiency for both robotic and manual surgery.
- Strengthening hospital IT security by helping security systems identify critical issues using human behavior and analytics.
- Discovering new drugs faster with promising applications in drug discovery, which must constantly outpace disease evolution.
- The use of machine learning to aid in diagnostics and clinical decision-making, enabling medical professionals to make faster and more accurate decisions.
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