Giving Compass' Take:

• W. Nicholson Price II explains the risks that AI in healthcare poses and some of the solutions that can help to ensure success. 

• How can funders work to increase information about the roles that AI could play in healthcare? 

• Learn how AI can aid in drug discovery

Although the field is quite young, AI has the potential to play at least four major roles in the health-care system:

  • Pushing boundaries of human performance.
  • Democratizing medical knowledge and excellence.
  • Automating drudgery in medical practice.
  • Managing patients and medical resources.

While AI offers a number of possible benefits, there also are several risks:

  • Injuries and error. The most obvious risk is that AI systems will sometimes be wrong, and that patient injury or other health-care problems may result.
  • Data availability. Training AI systems requires large amounts of data from sources such as electronic health records, pharmacy records, insurance claims records, or consumer-generated information like fitness trackers or purchasing history.
  • Privacy concerns. The requirement of large datasets creates incentives for developers to collect such data from many patients.
  • Bias and inequality. AI systems learn from the data on which they are trained, and they can incorporate biases from those data.
  • Professional realignment. Some medical specialties, such as radiology, are likely to shift substantially as much of their work becomes automatable.
  • The nirvana fallacy. The nirvana fallacy posits that problems arise when policymakers and others compare a new option to perfection, rather than the status quo.

There are several ways we can deal with possible risks of health-care AI:

  • Data generation and availability. Several risks arise from the difficulty of assembling high-quality data in a manner consistent with protecting patient privacy. One set of potential solutions turns on government provision of infrastructural resources for data, ranging from setting standards for electronic health records to directly providing technical support for high-quality data-gathering efforts in health systems that otherwise lack those resources.
  • Quality oversight. Oversight of AI-system quality will help address the risk of patient injury.
  • Provider engagement and education. The integration of AI into the health system will undoubtedly change the role of health-care providers.

Read the full article about AI in health care by W. Nicholson Price II at Brookings.