Your browser doesn't support javascript.
loading
The unintended consequences of artificial intelligence in paediatric radiology.
Ciet, Pierluigi; Eade, Christine; Ho, Mai-Lan; Laborie, Lene Bjerke; Mahomed, Nasreen; Naidoo, Jaishree; Pace, Erika; Segal, Bradley; Toso, Seema; Tschauner, Sebastian; Vamyanmane, Dhananjaya K; Wagner, Matthias W; Shelmerdine, Susan C.
Affiliation
  • Ciet P; Department of Radiology and Nuclear Medicine, Erasmus MC - Sophia's Children's Hospital, Rotterdam, The Netherlands.
  • Eade C; Department of Medical Sciences, University of Cagliari, Cagliari, Italy.
  • Ho ML; Royal Cornwall Hospitals Trust, Truro, Cornwall, UK.
  • Laborie LB; University of Missouri, Columbia, MO, USA.
  • Mahomed N; Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway.
  • Naidoo J; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
  • Pace E; Department of Radiology, University of Witwatersrand, Johannesburg, South Africa.
  • Segal B; Paediatric Diagnostic Imaging, Dr J Naidoo Inc., Johannesburg, South Africa.
  • Toso S; Envisionit Deep AI Ltd, Coveham House, Downside Bridge Road, Cobham, UK.
  • Tschauner S; Department of Diagnostic Radiology, The Royal Marsden NHS Foundation Trust, London, UK.
  • Vamyanmane DK; Department of Radiology, University of Witwatersrand, Johannesburg, South Africa.
  • Wagner MW; Pediatric Radiology, Children's Hospital, University Hospitals of Geneva, Geneva, Switzerland.
  • Shelmerdine SC; Division of Paediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria.
Pediatr Radiol ; 54(4): 585-593, 2024 Apr.
Article in En | MEDLINE | ID: mdl-37665368
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.
Subject(s)
Key words

Full text: 1 Database: MEDLINE Main subject: Radiology / Artificial Intelligence Type of study: Prognostic_studies Limits: Child / Humans Language: En Journal: Pediatr Radiol Year: 2024 Type: Article Affiliation country: Netherlands

Full text: 1 Database: MEDLINE Main subject: Radiology / Artificial Intelligence Type of study: Prognostic_studies Limits: Child / Humans Language: En Journal: Pediatr Radiol Year: 2024 Type: Article Affiliation country: Netherlands