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.
Accountable Deep-Learning-Based Vision Systems for Preterm Infant Monitoring
Simona Tiribelli;Alessandro Cacciatore;Benedetta Giovanola;Emanuele Frontoni;
2023-01-01
Abstract
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.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Accountable_Deep-Learning-Based_Vision_Systems_for_Preterm_Infant_Monitoring.pdf
solo utenti autorizzati
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
DRM non definito
Dimensione
2.2 MB
Formato
Adobe PDF
|
2.2 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
