Machine learning in medication prescription: A systematic review

Iancu, Alexa; Leb, Ines; Prokosch, Hans-Ulrich; Rödle, Wolfgang

doi:10.1016/j.ijmedinf.2023.105241

Cited by 1 publication

(1 citation statement)

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“…A common approach in MLHC is using clinical data to predict variables corresponding with a clinician's decision, such as the diagnostic label of an X-ray [84,85], or the decision to prescribe a medication [86]. Such approaches implicitly treat that clinical assessment as the ground truth, and in so doing establish contemporary clinician-level performance as a ceiling.…”

Section: Moving Past the Status Quomentioning

confidence: 99%

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Balagopalan,

Baldini,

Celi

et al. 2024

PLOS Digit Health

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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.

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Section: Moving Past the Status Quomentioning

confidence: 99%