We have computing capabilities that were unimaginable just a few decades ago, but although AI in its myriad forms is pushing the boundaries even further, the true promise of these technologies in healthcare won’t be realized unless they are incorporated into current systems responsibly.
That’s the view in a recent commentary of Steven Wolfe, Executive Director at the Baltimore, MD-based Alliance for Artificial Intelligence in Healthcare.
Interestingly, Wolfe’s father ended up in IT circles back in the 1960’s, so he brings a longterm perspective to the discussion that includes an understanding of both the technological advances and how those advances have been incorporated into numerous sectors, including healthcare.
The bottom line, he says, is that although IT in general, and AI specifically, continue to have the potential to bring significant benefits, the key to realizing that potential “for improving people’s healthcare is not simply the technical task of applying algorithms to data. Privacy, safety, equity, and how societies bear and distribute the costs of care are social imperatives that must be addressed in the process.”
Noting that “(d)eveloped societies have highly regulated healthcare systems,” Wolfe argues that “AI/ML can improve care only if they are integrated into these complex systems in a way that addresses these social imperatives and fits within a sound regulatory framework. But we must bring their capabilities to bear in a responsible and forward-looking manner.”
To that end, the rise of AI in healthcare brings “the need for a new regulatory framework” that will facilitate the safe and orderly use of numerous new technologies. That framework, Wolfe says, should revolve around two core tenets.
First up “is the regulation of products, including diagnostics and devices, with AI/ML software at the heart of their operation. To address this need, the Food and Drug Administration’s (FDA) Centre for Devices and Radiological Health (CDRH) has created a Digital Health Centre of Excellence to work with industry and other stakeholders to develop the standards and concepts to enable effective regulation.”
On a more tangible level, Wolfe’s second priority is to take “advantage of AI/ML’s ability to make the drug development process quicker and less expensive. As always, the FDA will approve drugs based on testing and assessments of safety and efficacy. But technology has enabled the use of real-world data (RWD) to inform those assessments and approvals like never before. . . . The challenge is how to turn this RWD into real-world evidence (RWE), that can be used to shape clinical trials, support pharmacovigilance, and efficacy assessments.”
In short, says Wolfe, “the more complex the endeavor, the more sophisticated the IT required to address it and the greater the potential payoff.” As well as the greater the responsibility to make sure to get it right.
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