Applications of AI are popping up throughout medicine. They are, after all, capable of gobbling mass quantities that would choke a mere human. Then AI finds patterns and makes inferences and predictions in seconds.

One manifestation of AI in medicine is the Command Monitoring Unit (CMU). The CMU monitors all the data outputs from a patient, runs it through the algorithm, and notifies doctors of a problem before a case can go sideways.

The advance warning is especially valuable in critical situations, like the onset of ventricular tachycardia. Minutes can mean the difference between life and death, and sometimes conventional alarms are missed.

At the Cleveland Clinic, the goal is to warn doctors with at least 60 minutes in advance of a serious cardiac event.

The ability to comb through mountains of data puts AI to work throughout the hospital, including screening babies for genetic disease. Most newborns are only evaluated for 20 of around 6000 genetic abnormalities. mGene, a program at Children’s National Hospital in Washington, D.C. scans photos of babies and diagnoses four common genetic diseases – Down, DiGeorge, Williams, and Noonan with accuracy rates over 90%.

Hospital Command Centers at top institutions are evaluating AI for multiple uses. In addition to intervention in critical cases, they’re used to calibrate staffing levels, predict nursing demand the onset of sepsis, and early signs of patient deterioration.

AI at work in ophthalmology

In ophthalmology, AI is being viewed as a valuable diagnostic partner with the doctor and a way to extract information from the surge in data from imaging devices.

In 2016, Google Brain 2016, deep learning AI synthesis stem taught itself to accurately detect retinal conditions, like diabetic retinopathy, and diabetic macular edema in fundus images. Other conditions tackled by AI now extend to pediatric cataract, glaucoma, keratoconus, corneal ectasia, and oculoplastic reconstruction.