Category: Artificial Intelligence and Healthcare

Doctors: AI is Not Ready for Prime Time (But it Soon Will)

Posted on by Devon Herrick

The New York Times talks to doctors who worry about whether artificial intelligence (AI) is up to the job of assisting in patient care.

In medicine, the cautionary tales about the unintended effects of artificial intelligence are already legendary.

There was the program meant to predict when patients would develop sepsis, a deadly bloodstream infection, that triggered a litany of false alarms. Another, intended to improve follow-up care for the sickest patients, appeared to deepen troubling health disparities.

AI is being tested in various ways. There is no Doctor AI yet, but the algorithms are embedded in decision-support software and even hardware that analyzes mammograms.

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Should Insurers Use AI to Manage Care? Yes, but with Checks & Balances

Posted on by Devon Herrick

Many states have passed laws limiting prior authorization. Physicians hate prior authorization and claim insurers and health plans use it to ration care. I tend to be more sympathetic to prior authorization because in an industry where patients are insulated from the cost of their care, there needs to be some checks and balances over unnecessary care and care that is unnecessarily expensive. I often tell the story about the time my wife unknowingly tried to schedule a CT scan at a hospital outpatient clinic near our house.

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AI Practicing Medicine

Posted on by John C. Goodman

Former FDA Commissioner Scott Gottlieb:

Artificial intelligence has the potential to help solve some of the most frustrating problems in health care. Clinicians may use it to stratify patients more precisely according to their personal risk and to identify increasingly tailored treatments that simultaneously account for a patient’s clinical history, genomic profile, and phenotypic characteristics. The combination of statistics and weighted observations in a neural network can be highly predictive. This is true even though each output for an individual patient is likely to differ from any other patient in a validation model, and even though the variables, when taken individually, are not likely to be nearly as predictive.

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