Why People Should Be Cautious of AI in Healthcare
Thu, April 22, 2021

Why People Should Be Cautious of AI in Healthcare

Experts argued that we need to be cautious in adopting AI in healthcare because it is still in its infancy and it needs to mature / Credits: Luis Louro via 123RF

 

Artificial intelligence has produced groundbreaking tools, applications, and studies that greatly helped the healthcare industry. For instance, AI has helped in the diagnostic analysis where AI systems can collect and analyze data sets on symptoms to diagnose the potential issue and offer treatment solutions. John Bailey, director of sales for the healthcare technology company Chetu Inc., stated that this can assist doctors in determining the illness or condition and allow for better, more responsive care.

“Since AI’s key benefit is in pattern detection, it can also be leveraged in identifying, and assist in containing, illness outbreaks and antibiotic resistance,” said Bailey.

However, many experts expressed their concern with the increasing adoption of AI in healthcare. José Morey, MD, a physician, AI expert, and former associate chief health officer for IBM Watson, stated that while AI has the potential to democratize healthcare in many ways, it is still in its infancy and it needs to mature. “Consumers should be wary of rushing to a new facility simply because they may be providing a new AI tool, especially if it is for diagnostics. There are really just a handful of physicians across the world that are practicing that understand the strengths and benefits of what is currently available,” he said. 

According to Healthline, an online site that covers all facets of physical and mental health openly and objectively, experts are concerned with AI bias. This could make AI systems only work in one very specific setting but won’t be compatible with large scale roll-out. Ray Walsh, a digital privacy expert at ProPrivacy, added that machine learning is only as good as the data sets the machines are working with.

Walsh is worried that the lack of diversity in the datasets used to train up medical AI could lead to algorithms discriminating the underrepresented demographics. “This can create AI that is prejudiced against certain people. As a result, AI could lead to prejudice against particular demographics based on things like high body mass index (BMI), race, ethnicity, or gender,” he added.