The unintended consequences of artificial intelligence in paediatric radiology

© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature..

Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession. In this article, we provide an overview of the latest opinions regarding AI ethics, bias, limitations, challenges and considerations that we should all contemplate in this exciting and expanding field, with a special attention to how this applies to the unique aspects of a paediatric population. By embracing AI technology and fostering a multidisciplinary approach, it is hoped that we can harness the power AI brings whilst minimising harm and ensuring a beneficial impact on radiology practice.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:54

Enthalten in:

Pediatric radiology - 54(2024), 4 vom: 29. Apr., Seite 585-593

Sprache:

Englisch

Beteiligte Personen:

Ciet, Pierluigi [VerfasserIn]
Eade, Christine [VerfasserIn]
Ho, Mai-Lan [VerfasserIn]
Laborie, Lene Bjerke [VerfasserIn]
Mahomed, Nasreen [VerfasserIn]
Naidoo, Jaishree [VerfasserIn]
Pace, Erika [VerfasserIn]
Segal, Bradley [VerfasserIn]
Toso, Seema [VerfasserIn]
Tschauner, Sebastian [VerfasserIn]
Vamyanmane, Dhananjaya K [VerfasserIn]
Wagner, Matthias W [VerfasserIn]
Shelmerdine, Susan C [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Child
Journal Article
Machine learning
Radiology

Anmerkungen:

Date Completed 03.04.2024

Date Revised 03.04.2024

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1007/s00247-023-05746-y

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM361630840