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This article employs analytical and empirical tools to dissect the complex relationship between secrecy, accountability, and innovation incentives in clinical decision software enabled by machine learning (ML-CD). Although secrecy can provide incentives for innovation, it can also diminish the ability of third parties to adjudicate risk and benefit responsibly. Our first aim is descriptive. We address how the interrelated regimes of intellectual property law, Food and Drug Administration (FDA)doi:10.1093/jlb/lsaa077 fatcat:stsc2ldz2jeqjdms6uiuvgteym