Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
Aparna Balagopalan,
Ioana Baldini,
Leo Anthony Celi
et al.
Abstract:Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper “Machine Learning that Matters”, which highlighted su… Show more
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