Dr. Tanuj Gupta, vice president at Cerner Intelligence, is an expert in healthcare artificial intelligence and machine learning. Part of his job is explaining, from his expert point of view, what he considers misconceptions with AI, especially misconceptions in healthcare.
In this interview with Healthcare IT News, Gupta discusses what he says are popular misconceptions with gender and racial bias in algorithms, AI replacing clinicians, and the regulation of AI in healthcare.
Q. In general terms, why do you think there are misconceptions about AI in healthcare, and why do they persist?
A. I’ve given more than 100 presentations on AI and ML in the past year. There’s no doubt these technologies are hot topics in healthcare that usher in great hope for the advancement of our industry.
While they have the potential to transform patient care, quality and outcomes, there also are concerns about the negative impact this technology could have on human interaction, as well as the burden they could place on clinicians and health systems.
Q. Should we be concerned about gender and racial bias in ML algorithms?
A. Traditionally, healthcare providers consider a patient’s unique situation when making decisions, along with information sources, such as their clinical training and experiences, as well as published medical research.
Now, with ML, we can be more efficient and improve our ability to examine large amounts of data, flag potential problems and suggest next steps for treatment. While this technology is promising, there are some risks. Although AI and ML are just tools, they have