Technology replacing MR hu, based on studies at Massachusetts General Hospital and MIT, showed that an artificial intelligence (AI) system was equal to or better than radiologists in reading mammograms for high-risk surgical cancer lesions. A year earlier, as reported by the Journal of the American Medical Association, Google proved that computers are similar to ophthalmologists by examining images of the retinas of diabetics. And recently, computer-controlled robots successfully performed intestinal surgery on a pig. The robot took longer than a human, but its seams were much better and more precise. and uniform with less chance of breakage, leakage, and infection.
Technology drivers believe that AI will lead to more evidence-based care, more personalized care and fewer errors. Of course, improving diagnostic and therapeutic outcomes are worthy goals and can lead to technology replacing MR. But AI is only as good as the people who program it and the system in which it operates. If we’re not careful, AI might not improve healthcare, but would inadvertently make many of the worst aspects of our current healthcare system worse.
Using machine and deep learning, AI systems analyze vast 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 payment claims, electronic medical records, medical images, genetic data, laboratory data, prescription data, clinical emails, and patient demographic information to empower AI models.
AI depends 100% on this data, and as with everything related to computing. A key concern of all of our health records is that they perfectly record a history of unwarranted and unwarranted disparities in access, treatments, and outcomes across the United States. .
According to a 2017 National Academy of Medicine report on health inequalities, people of color continue to have poorer outcomes in infant mortality, obesity, heart disease, cancer, stroke, HIV/AIDS and all-cause mortality. Surprisingly, Alaska Natives suffer 60 percent more infant mortality than whites.Worse still, AIDS mortality among African Americans is actually increasing. Even among whites, there is significant geographic variation in outcomes and mortality. Bias due to socioeconomic status can be amplified by including patient-generated data from expensive sensors, phones, and social media.
Another major challenge with £ is that many clinicians make assumptions and treatment decisions that are not clearly documented as structured data. sick patients using the numbers entered into the computer programs. This results in some patients being treated differently than others for reasons that are difficult to ascertain from electronic medical record data.This clinical judgment is not well represented by the data.
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