Laying the foundations for artificial intelligence in health
Artificial intelligence (AI) has the potential to make health care more effective,
efficient and equitable. AI applications are on the rise, from clinical decision-making
and public health, to biomedical research and drug development, to health system administration
and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also
a reality check, highlighting the limits of existing AI systems. Most AI in health
is actually artificial narrow intelligence, designed to accomplish very specific tasks
on previously curated data from single settings. In the real world, health data are
not always available, standardised, or easily shared. Limited data hinders the ability
of AI tools to generate accurate information for diverse populations with potentially
very complex conditions. Having appropriate patient data is critical for AI tools
because decisions based on models with skewed or incomplete data can put patients
at risk. Policy makers should beware of the hype surrounding AI and identify and focus
on real problems and opportunities that AI can help address. In setting the foundations
for AI to help achieve health policy objectives, one key priority is to improve data
quality, interoperability and access in a secure way through better data governance.
More broadly, policy makers should work towards implementing and operationalising
the OECD AI Principles, as well as investing in technology and human capital. Strong
policy frameworks based on inclusive and extensive dialogue among all stakeholders
are also key to ensure AI adds value to patients and to societies. AI that influences
clinical and public health decisions should be introduced with care. Ultimately, high
expectations must be managed, but real opportunities should be pursued.
Published on June 11, 2021
In series:OECD Health Working Papersview more titles