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You could make the case there will be no industry more significantly impacted by advances in artificial intelligence than healthcare and medicine. If we think about artificial intelligence as a tool that can emulate human behavior, then there is no limit its applications in a field that requires constant human involvement.

AI is already being used to significantly lower the number of misdiagnoses and human error in healthcare. A 2016 report by Johns Hopkins found that 10 percent of all U.S. deaths were caused by medical error, making it the third highest cause of death in the country. By applying AI tools to the healthcare industry, we are making great strides in improving diagnostic tools, analyzing large case loads and medical histories more efficiently and accurately, and better predicting illnesses.

Specifically, we are seeing AI used to analyze complex images such as mammograms and CT scans, aiding radiologists in cancer detection. Hospitals spend enormous amounts of money on administrative resources; AI can speed up data crunching and streamline organization, cutting down time spent on repetitive tasks. Creating personalized prognoses and treatment plans are easier than ever with AI technology. And drug development stands to improve greatly, with AI-supported data analysis and machine learning; AI can determine which drug components are viable and which aren’t, accelerating drug and cure research.

But as with the infiltration of AI into other industries, its presence in healthcare has raised several substantial concerns. For AI to reach its maximum potential in healthcare, it needs to collect enormous quantities of data—data that comes at the cost of patient privacy. Moreover, unconscious bias is a serious risk in healthcare. A 2019 study published in Science found evidence of racial bias in a widely used risk-prediction algorithm. It was observed that Black patients received lower risk scores by the algorithms even when they were sicker than comparable white patients. In other words, Black patients were receiving lower-quality care with less favorable outcomes.

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