More than one-third of healthcare industry executives believe the pace at which they are implementing AI is too slowly.

That’s according to a new study from KPMG, ”Living in an AI World: Achievements and Challenges of Artificial Intelligence Across Five Industries.” 

At the same time, however, 53% of respondents said the industry is actually ahead of most others in AI adoption, though they also admitted the transition should be happening much faster.

Among the primary reasons for the slow pace were a lack of training for employees, as well as widespread concern around privacy issues.

“The pace with which hospital systems have adopted AI and automation programs has dramatically increased since 2017,” said Melissa Edwards, managing director, digital enablement, at KPMG. “Virtually all major healthcare providers are moving ahead with pilots or programs in these areas. The medical literature is showing support of AI’s power as a tool to help clinicians.”

Indeed, according to an overwhelming majority of respondents (89%), AI already is creating efficiencies in their systems, and 91% believe it is increasing patient access to care.

“My general observation is that more of the AI-related services and solutions being advanced in healthcare today are largely in the clinical, patient-facing space,” Edwards said. “Basic forms of automation are proving to be the ‘gateway drug’ to advanced forms of AI – such as scanning documents to determine the urgency of a referral. Applying AI to make earlier diagnoses of critical illnesses is a key area.”

Despite the desire for quicker implementation of the new technologies, 68% of survey respondents are confident AI eventually will be effective in diagnosing patient illnesses and conditions, with 47% believing that diagnostics will have a significant impact soon – within the next two years. They also anticipate gains in process automation, with 40% seeing X-rays and CT scans being handled robotically.

At the same time, many are concerned about the impact of AI and other technologies on the healthcare sector, with more than half of respondents believing that AI has increased, rather than reduced, the costs of healthcare.

According to Edwards, one problem slowing implementation is a continued dearth of individuals who “speak” the language of AI.

“Comprehending the full range of AI technology, and how best to apply it in a healthcare setting, is a learned skill that grows out of pilots and tests,” she observed. “Building an AI-ready workforce requires a wholesale change in the approach to training and how to acquire talent. Having people who understand how AI can solve big, complex problems is critical.”

As for concerns about cost, Edwards said healthcare decision-makers are still struggling to determine where to place their AI best bets: “The question is, ‘Where do I put my AI efforts to get the greatest gain for the business? Trying to assess what ROI will look like is a very relevant point as they embark on their AI journey.”