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.
A number with no recorded reason may end up deciding for a patient Leer más »
The most difficult thing to build into an artificial intelligence is not accuracy. It is restraint. Contemporary systems are rewarded, structurally, for producing an answer — a label, a score, a confident sentence — because a fluent reply reads as competence and a hesitation reads as failure. Yet in the places where these systems will
Not yet: the third answer in machine intelligence Leer más »
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
External validation is not a bureaucratic detail Leer más »
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
A medical algorithm must not confuse cost with health Leer más »