Open AI

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Artificial intelligence in medicine: Is the genie out of the bottle?

It is probably a given that artificial intelligence (AI) will become an integral part of healthcare delivery and of our public health infrastructure. What is not a given is that we will easily reach that point, and maintain progress in a way that maximizes its effectiveness in achieving the goals we have come to expect of it – efficient and improved healthcare and public health systems. In other words, making the health of people better in a cost-effective way. Responsible commentators have already begun to question the value of AI in medicine.

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Radiology Initiatives Illustrate Uses for Open Data and Open AI research

Fans of data in health care often speculate about what clinicians and researchers could achieve by reducing friction in data sharing. What if we had easy access to group repositories, expert annotations and labels, robust and consistent metadata, and standards without inconsistencies? Since 2017, the Radiological Society of North America (RSNA) has been displaying a model for such data sharing. That year marked RSNA's first AI challenge. RSNA has worked since then to make the AI challenge an increasingly international collaboration. Organizers of each challenge curate and annotate medical imaging studies and ask the research community to come up with models to answer important questions.

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