Accountable Deep-Learning-Based Vision Systems for Preterm Infant Monitoring

被引：2

作者：

Migliorelli, Lucia ^{[1
]}

Tiribelli, Simona ^{[2
]}

Cacciatore, Alessandro ^{[3
]}

Giovanola, Benedetta ^{[2
,7
]}

Frontoni, Emanuele ^{[4
]}

Moccia, Sara ^{[5
,6
]}

机构：

[1] Univ Politecn Marche, Dept Informat Engn, I-60121 Ancona, Italy

[2] Univ Macerata, Eth, I-62100 Macerata, Italy

[3] Univ Macerata, Dept Humanities, I-62100 Macerata, Italy

[4] Univ Macerata, Comp Sci, Macerata, Italy

[5] BioRobot Inst, Bioengn, I-56127 Pisa, Italy

[6] Dept Excellence Robot & Scuola Super St Anna, I-56127 Pisa, Italy

[7] Tufts Univ, Medford, MA USA

来源：

COMPUTER | 2023年 / 56卷 / 05期

关键词：

Ethics; Pediatrics; Machine vision; Monitoring; CEREBRAL-PALSY;

D O I：

10.1109/MC.2023.3235987

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work proposes an ethical framework that highlights possible ethical risks in the design and use of deep-learning-based vision systems for monitoring infants' movements in neonatal intensive care units. We discuss biases and ways to mitigate them for promoting accountable systems in clinical practice.

引用

页码：84 / 93

页数：10

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