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] |
---|
Links: |
---|
Themen: |
Artificial intelligence |
---|
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 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | NLM361630840 | ||
003 | DE-627 | ||
005 | 20240403234930.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231226s2024 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1007/s00247-023-05746-y |2 doi | |
028 | 5 | 2 | |a pubmed24n1362.xml |
035 | |a (DE-627)NLM361630840 | ||
035 | |a (NLM)37665368 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Ciet, Pierluigi |e verfasserin |4 aut | |
245 | 1 | 4 | |a The unintended consequences of artificial intelligence in paediatric radiology |
264 | 1 | |c 2024 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 03.04.2024 | ||
500 | |a Date Revised 03.04.2024 | ||
500 | |a published: Print-Electronic | ||
500 | |a Citation Status MEDLINE | ||
520 | |a © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. | ||
520 | |a 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 | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Artificial intelligence | |
650 | 4 | |a Child | |
650 | 4 | |a Machine learning | |
650 | 4 | |a Radiology | |
700 | 1 | |a Eade, Christine |e verfasserin |4 aut | |
700 | 1 | |a Ho, Mai-Lan |e verfasserin |4 aut | |
700 | 1 | |a Laborie, Lene Bjerke |e verfasserin |4 aut | |
700 | 1 | |a Mahomed, Nasreen |e verfasserin |4 aut | |
700 | 1 | |a Naidoo, Jaishree |e verfasserin |4 aut | |
700 | 1 | |a Pace, Erika |e verfasserin |4 aut | |
700 | 1 | |a Segal, Bradley |e verfasserin |4 aut | |
700 | 1 | |a Toso, Seema |e verfasserin |4 aut | |
700 | 1 | |a Tschauner, Sebastian |e verfasserin |4 aut | |
700 | 1 | |a Vamyanmane, Dhananjaya K |e verfasserin |4 aut | |
700 | 1 | |a Wagner, Matthias W |e verfasserin |4 aut | |
700 | 1 | |a Shelmerdine, Susan C |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Pediatric radiology |d 1973 |g 54(2024), 4 vom: 29. Apr., Seite 585-593 |w (DE-627)NLM000468924 |x 1432-1998 |7 nnns |
773 | 1 | 8 | |g volume:54 |g year:2024 |g number:4 |g day:29 |g month:04 |g pages:585-593 |
856 | 4 | 0 | |u http://dx.doi.org/10.1007/s00247-023-05746-y |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 54 |j 2024 |e 4 |b 29 |c 04 |h 585-593 |