Why is the health care industry slow in adopting artificial intelligence?
A new report explores several reasons, including barriers to entry, biased algorithms, and limited access to data.
News of CVS Pharmacy’s entry into the metaverse has spurred increased interest in how augmented reality, virtual reality, and artificial intelligence will reshape the health care landscape.
A new report from Brookings focuses specifically on AI and acknowledges that it has the potential to make “a large impact” on health care. In the report, Avi Goldfarb (Rotman Chair in Artificial Intelligence and Healthcare, as well as Professor of Marketing at the Rotman School of Management at the University of Toronto) and Florenta Teodoridis (Assistant Professor of Management and Organization at the University of Southern California’s Marshall School of Business) cite numerous academic and industry conferences dedicated to the topic, as well as major medical journals and reports from nonprofit organizations, private consultancies, and the U.S. government.
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For context, AI is “the ability of a computer or a computer-controlled robot to perform tasks that are usually done by humans, as they require human intelligence,” according to KDNuggets.com, a leading website covering AI and machine learning.
But, as Goldfarb and Teodoridis suggest, the health care industry might not be ready to completely embrace AI just yet. “Despite the hype and potential, there has been little AI adoption in health care,” they write, noting that “an early glance into AI adoption patterns [can be] observed through U.S. job advertisements that require AI-related skills.”
Relying on data collected by analytics software company Burning Glass Technologies and based on job advertisements from more than 40,000 online job boards and company websites, Goldfarb and Teodoridis explain that “health care and social assistance” is the penultimate industry on the list of job postings requiring AI-related skills. The only industry with fewer such postings? Construction.
“Even for the relatively skilled job postings in hospitals, which include doctors, nurses, medical technicians, research lab workers, and managers, only approximately 1 in 1,250 job posting required AI skills,” they write. “This is lower than other skilled industries such as professional, scientific, or technical services, finance and insurance, and educational services.”
Why?
Goldfarb and Teodoridis note several reasons — including barriers to adoption of AI in health care (like electronic medical records, they add, AI adoption is more likely to begin with large companies and in big cities), a lack of trust in AI based on faulty or biased algorithms, limited access to data that is often challenging to access and collect, and multiple regulatory barriers.
“Policymakers can help generate useful adoption with some innovative approaches to privacy and the path to regulatory approval,” Goldfarb and Teodoridis conclude. “However, it might be the familiar tools that are most useful: clarify the rules, fund research, and enable competition.”
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