GAO: Artificial Intelligence in Healthcare: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics

What GAO Found

Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases — certain cancers, diabetic retinopathy, Alzheimer’s disease, heart disease, and COVID-19 —with most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). However, these ML technologies have generally not been widely adopted.

Academic, government, and private sector researchers are working to expand the capabilities of ML-based medical diagnostic technologies. In addition, GAO identified three broader emerging approaches—autonomous, adaptive, and consumer-oriented ML-diagnostics—that can be applied to diagnose a variety of diseases. These advances could enhance medical professionals’ capabilities and improve patient treatments but also have certain limitations. For example, adaptive technologies may improve accuracy by incorporating additional data to update themselves, but automatic incorporation of low-quality data may lead to inconsistent or poorer algorithmic performance…

We identified several challenges affecting the development and adoption of ML in medical diagnostics:

  • Demonstrating real-world performance across diverse clinical settings and in rigorous studies.
  • Meeting clinical needs, such as developing technologies that integrate into clinical workflows.
  • Addressing regulatory gaps, such as providing clear guidance for the development of adaptive algorithms.

These challenges affect various stakeholders including technology developers, medical providers, and patients, and may slow the development and adoption of these technologies.

GAO developed three policy options that could help address these challenges or enhance the benefits of ML diagnostic technologies. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry…

Access the full 103-page report here.



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