In a new perspective piece “Transparency and reproducibility in artificial intelligence” published this week in the journal Nature, an international group of scientists including CUNY Graduate School of Public Health and Health Policy (CUNY SPH) Associate Professor Levi Waldron raised concerns about the lack of transparency in publication of artificial intelligence algorithms for health applications.
The authors raise concerns about a recent publication in which a group including Google Health reported using artificial intelligence to diagnose breast cancer from mammogram images more accurately than expert human radiologists. The authors contend that restrictive data access procedures, lack of published computer code, and unreported model parameters make it impractically difficult for any other researchers to confirm or extend this work. The piece also highlights tensions over what are appropriate measures to protect patient privacy while allowing the broader research community to contribute methodology and to correct potential errors that could set back progress to the detriment of other patients.
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