Artificial intelligence (AI) technologies have improved rapidly over the past decade, largely driven by advances in machine learning, which is closely related to data science and statistical prediction. Several aspects of the health care system involve prediction, including diagnosis, treatment, administration, and operations.This connection between machine learning’s capabilities and needs of the health care system has led to widespread speculation that AI will have a large impact on health care.
For instance, Eric Topol’s “Deep Medicine: How Artificial Intelligence can make Health Care Human Again,” highlights AI’s potential to improve the lives of doctors and patients. The progress and promise of clinical AI algorithms range from image-based diagnosis in radiology and dermatology to surgery, and from patient monitoring to genome interpretation and drug discovery. There are dozens of academic and industry conferences dedicated to describing the opportunity for AI in health care. For example, AI Med and the Ai4 Healthcare Summit are two of many conferences dedicated to facilitating the adoption of AI in health care organizations. ML4H and CHIL, in contrast, provide forums for scholars to present the latest advances in academic research. The major medical journals have all dedicated space to research articles and editorials about AI. These sentiments have been detailed in numerous reports from nonprofits, private consultancies, and governments including the World Health Organization and the U.S. Government Accountability Office.
In 2019, 11% of American workers were employed in health care, and health care expenditures accounted for over 17% of gross domestic product. U.S. health care spending is higher per capita than other OECD countries. If AI technologies have a similar impact on healthcare as in other industries such as retail and financial services, then health care can become more effective and more efficient, improving the daily lives of millions of people.
However, despite the hype and potential, there has been little AI adoption in health care. We provide an early glance into AI adoption patterns as observed through U.S. job advertisements that require AI-related skills. Job advertisements provide a window into technology diffusion patterns. As a technology evolves and spreads across application sectors, labor demand adjusts to include the type of skills required to adopt the technology, up to a point when the technology is sufficiently pervasive that such skills are no longer explicitly listed in job postings.
Figure 1 shows the percentage of U.S. job advertisements that require AI-related skills by industry (defined by two-digits NAICS codes) for the years 2015-2018. This data, collected by Burning Glass Technologies, is based on over 40,000 online job boards and company websites. At the top of the figure is the information industry, which includes large technology companies such as Google and Microsoft. More than 1 in 100 of all jobs in the information industry require some AI-related skills. Professional services and finance also rank relatively high. The next few industries—manufacturing, mining, and agriculture—may be a surprise to those that have been less focused on how AI has enabled opportunities in robotics and distribution. At the bottom is construction. Just above construction is health care and social assistance, where 1 in 1,850 jobs required AI skills. The relatively low rate of AI in job postings is not driven by social assistance. Even for the relatively-skilled job postings in hospitals, which includes doctors, nurses, medical technicians, research lab workers, and managers, only approximately 1 in 1,250 job postings required AI skills. This is lower than other skilled industries such as professional, scientific, or technical services, finance and insurance, and educational services.