Artificial Intelligence and Clinical Decisions: Major Challenges Ahead?
Artificial intelligence (AI) is disrupting industries across the globe, and in healthcare it has the potential to enhance patient outcomes, lower costs, support clinical decision-making and improve the quality of life for healthcare providers. But adoption of the technology is not without regulatory, legal, data and adoption challenges, according to a paper released by the Duke-Margolis Center for Health Policy.
“Integrating AI into health care safely and effectively will need to be a careful process, requiring policymakers and stakeholders to strike a balance between the essential work of safeguarding patients while ensuring that innovators have access to the tools they need to succeed in making products that improve the public health,” stated Greg Daniel, PhD, MPH, Deputy Director for Policy at Duke-Margolis in a press release.
The paper, titled, “Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care”, points to a few priorities that must be dealt with in order to enable effective adoption of AI in healthcare. These areas include regulatory clarity in how FDA examines patient risk when providers use “black box” AI-enabled software; consistency of clinical data, interoperability and protecting patient privacy; and ability to demonstrate value in improving provider system efficiency, key outcomes and cost.
The report, which is intended to help developers, clinicians, regulators and policy makers is available online.
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