TY - JOUR
T1 - Machine learning for healthcare that matters
T2 - Reorienting from technical novelty to equitable impact
AU - Balagopalan, Aparna
AU - Baldini, Ioana
AU - Celi, Leo Anthony
AU - Gichoya, Judy
AU - McCoy, Liam G.
AU - Naumann, Tristan
AU - Shalit, Uri
AU - van der Schaar, Mihaela
AU - Wagstaff, Kiri L.
N1 - Publisher Copyright: © 2024 Balagopalan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/4
Y1 - 2024/4
N2 - 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 such structural issues in the ML community at large, and offered a series of clearly defined “Impact Challenges” to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare—the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.
AB - 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 such structural issues in the ML community at large, and offered a series of clearly defined “Impact Challenges” to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare—the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.
UR - http://www.scopus.com/inward/record.url?scp=85201587782&partnerID=8YFLogxK
U2 - https://doi.org/10.1371/journal.pdig.0000474
DO - https://doi.org/10.1371/journal.pdig.0000474
M3 - مقالة
C2 - 38620047
SN - 2767-3170
VL - 3
JO - PLOS Digital Health
JF - PLOS Digital Health
IS - 4
M1 - e0000474
ER -