Research ExplorerPublicationsMachine learning in clinical decision making

Machine learning in clinical decision making

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Machine learning in clinical decision making. / Adlung, Lorenz; Cohen, Yotam; Mor, Uria; Elinav, Eran.

In: MED-CAMBRIDGE, Vol. 2, No. 6, 11.06.2021, p. 642-665.

Research output: SCORING: Contribution to journal › SCORING: Review article › Research

Harvard

Adlung, L, Cohen, Y, Mor, U & Elinav, E 2021, 'Machine learning in clinical decision making', MED-CAMBRIDGE, vol. 2, no. 6, pp. 642-665. https://doi.org/10.1016/j.medj.2021.04.006

APA

Adlung, L., Cohen, Y., Mor, U., & Elinav, E. (2021). Machine learning in clinical decision making. MED-CAMBRIDGE, 2(6), 642-665. https://doi.org/10.1016/j.medj.2021.04.006

Vancouver

Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. MED-CAMBRIDGE. 2021 Jun 11;2(6):642-665. https://doi.org/10.1016/j.medj.2021.04.006

Bibtex

@article{44b23016c92c41af9efb35f8096e82e3,
title = "Machine learning in clinical decision making",
abstract = "Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and secondary prevention. However, a multitude of technological, medical, and ethical considerations are critical in machine-learning utilization, including the necessity for careful validation of machine-learning-based technologies in real-life contexts, unbiased evaluation of benefits and risks, and avoidance of technological over-dependence and associated loss of clinical, ethical, and social-related decision-making capacities. Other challenges include the need for careful benchmarking and external validations, dissemination of end-user knowledge from computational experts to field users, and responsible code and data sharing, enabling transparent assessment of pipelines. In this review, we highlight key promises and achievements in integration of machine-learning platforms into clinical medicine while highlighting limitations, pitfalls, and challenges toward enhanced integration of learning systems into the medical realm.",
author = "Lorenz Adlung and Yotam Cohen and Uria Mor and Eran Elinav",
year = "2021",
month = jun,
day = "11",
doi = "10.1016/j.medj.2021.04.006",
language = "English",
volume = "2",
pages = "642--665",
journal = "MED-CAMBRIDGE",
issn = "2666-6340",
publisher = "Cell Press",
number = "6",

}

RIS

TY - JOUR

T1 - Machine learning in clinical decision making

AU - Adlung, Lorenz

AU - Cohen, Yotam

AU - Mor, Uria

AU - Elinav, Eran

PY - 2021/6/11

Y1 - 2021/6/11

N2 - Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and secondary prevention. However, a multitude of technological, medical, and ethical considerations are critical in machine-learning utilization, including the necessity for careful validation of machine-learning-based technologies in real-life contexts, unbiased evaluation of benefits and risks, and avoidance of technological over-dependence and associated loss of clinical, ethical, and social-related decision-making capacities. Other challenges include the need for careful benchmarking and external validations, dissemination of end-user knowledge from computational experts to field users, and responsible code and data sharing, enabling transparent assessment of pipelines. In this review, we highlight key promises and achievements in integration of machine-learning platforms into clinical medicine while highlighting limitations, pitfalls, and challenges toward enhanced integration of learning systems into the medical realm.

AB - Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and secondary prevention. However, a multitude of technological, medical, and ethical considerations are critical in machine-learning utilization, including the necessity for careful validation of machine-learning-based technologies in real-life contexts, unbiased evaluation of benefits and risks, and avoidance of technological over-dependence and associated loss of clinical, ethical, and social-related decision-making capacities. Other challenges include the need for careful benchmarking and external validations, dissemination of end-user knowledge from computational experts to field users, and responsible code and data sharing, enabling transparent assessment of pipelines. In this review, we highlight key promises and achievements in integration of machine-learning platforms into clinical medicine while highlighting limitations, pitfalls, and challenges toward enhanced integration of learning systems into the medical realm.

U2 - 10.1016/j.medj.2021.04.006

DO - 10.1016/j.medj.2021.04.006

M3 - SCORING: Review article

C2 - 35590138

VL - 2

SP - 642

EP - 665

JO - MED-CAMBRIDGE

JF - MED-CAMBRIDGE

SN - 2666-6340

IS - 6

ER -