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A number with no recorded reason may end up deciding for a patient

Transparency in artificial intelligence has become a question of provenance. Provenance tells you where a piece of content came from; it does not tell you why what it asserts was decided.

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External validation is not a bureaucratic detail

Systems for diagnosis, prognosis and imaging have repeatedly been deployed or promoted on the strength of performance that proved fragile under independent testing. A model that performs well in the hospital where it was born has proven only one thing: that it works at home. Consider the most instructive failure in recent clinical AI. The

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A medical algorithm must not confuse cost with health

In 2019, a research team led by the physician and economist Ziad Obermeyer reverse-engineered a commercial algorithm already running quietly across the United States health system. The tool, sold by Optum, helped decide which patients — out of a population of roughly 200 million a year — would be flagged for extra medical attention. To

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