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SHR Neuro Cancer Cardio Lipid Metab Microb

Ciet, P; Eade, C; Ho, ML; Laborie, LB; Mahomed, N; Naidoo, J; Pace, E; Segal, B; Toso, S; Tschauner, S; Vamyanmane, DK; Wagner, MW; Shelmerdine, SC.
The unintended consequences of artificial intelligence in paediatric radiology.
Pediatr Radiol. 2024; 54(4):585-593 Doi: 10.1007/s00247-023-05746-y
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Co-authors Med Uni Graz: Tschauner Sebastian
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Abstract:: 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.
Find related publications in this database (using NLM MeSH Indexing): Child - administration & dosage; Humans - administration & dosage; Artificial Intelligence - administration & dosage; Radiology - administration & dosage; Societies, Medical - administration & dosage
Find related publications in this database (Keywords): Artificial intelligence; Child; Machine learning; Radiology