Will Robots replace Doctors?

 Case Study

       A  2017 study out of the Massachusetts General Hospital and MIT showed that an artificial intelligence (AI) system was equal or better than radiologists at reading mammograms for high risk cancer lesions needing surgery. A year earlier, and reported by the Journal of the American Medical Association, Google showed that computers are similar to ophthalmologists at examining retinal images of diabetics.  And recently, computer-controlled robots performed intestinal surgery successfully on a pig. While the robot took longer than a human, its sutures were much better more precise and uniform with fewer chances for breakage, leakage, and infection. Tech boosters believe that AI will lead to more evidence-based care, more personalized care, and fewer errors.

Of course, improving diagnostic and therapeutic outcomes are laudable goals. But AI is only as good as the humans programming it and the system in which it operates. If we are not careful, AI could not make health care better, but instead unintentionally exacerbate many of the worst aspects of our current health care system.

Using deep and machine learning, AI systems analyze enormous amounts of data to make predictions and recommend interventions. Advances in computing power have enabled the creation and cost-effective analysis of large datasets of payer claims, electronic health record data, medical images, genetic data, laboratory data, prescription data, clinical emails, and patient demographic information to power AI models.

AI is 100 percent dependent on this data, and as with everything in computing, “garbage in, garbage out,” as the saying goes. A major concern about all our health care datasets is that they perfectly record a history of unjustified and unjust disparities in access, treatments, and outcomes across the United States.