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The unintended consequences of artificial intelligence in paediatric radiology

  • ESPR Belgrade 2023 - Postgraduate Course and Taskforce Lectures
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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.

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Authors and Affiliations

  1. Department of Radiology and Nuclear Medicine, Erasmus MC – Sophia’s Children’s Hospital, Rotterdam, The Netherlands

    Pierluigi Ciet

  2. Department of Medical Sciences, University of Cagliari, Cagliari, Italy

    Pierluigi Ciet

  3. Royal Cornwall Hospitals Trust, Truro, Cornwall, UK

    Christine Eade

  4. University of Missouri, Columbia, MO, USA

    Mai-Lan Ho

  5. Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway

    Lene Bjerke Laborie

  6. Department of Clinical Medicine, University of Bergen, Bergen, Norway

    Lene Bjerke Laborie

  7. Department of Radiology, University of Witwatersrand, Johannesburg, South Africa

    Nasreen Mahomed & Bradley Segal

  8. Paediatric Diagnostic Imaging, Dr J Naidoo Inc., Johannesburg, South Africa

    Jaishree Naidoo

  9. Envisionit Deep AI Ltd, Coveham House, Downside Bridge Road, Cobham, UK

    Jaishree Naidoo

  10. Department of Diagnostic Radiology, The Royal Marsden NHS Foundation Trust, London, UK

    Erika Pace

  11. Pediatric Radiology, Children’s Hospital, University Hospitals of Geneva, Geneva, Switzerland

    Seema Toso

  12. Division of Paediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria

    Sebastian Tschauner

  13. Department of Pediatric Radiology, Indira Gandhi Institute of Child Health, Bangalore, India

    Dhananjaya K. Vamyanmane

  14. Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for Sick Children, Toronto, Canada

    Matthias W. Wagner

  15. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    Matthias W. Wagner

  16. Department of Neuroradiology, University Hospital Augsburg, Augsburg, Germany

    Matthias W. Wagner

  17. Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, London, WC1H 3JH, UK

    Susan C. Shelmerdine

  18. Great Ormond Street Hospital for Children, UCL Great Ormond Street Institute of Child Health, London, UK

    Susan C. Shelmerdine

  19. NIHR Great Ormond Street Hospital Biomedical Research Centre, 30 Guilford Street, Bloomsbury, London, UK

    Susan C. Shelmerdine

  20. Department of Clinical Radiology, St George’s Hospital, London, UK

    Susan C. Shelmerdine

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  1. Pierluigi Ciet
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  2. Christine Eade
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  3. Mai-Lan Ho
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Contributions

S. C. S. conceived, supervised and supported the study. All authors performed literature review and drafted the initial manuscript for their allocated subsection. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Susan C. Shelmerdine.

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Ethical approval was not applicable to this review article.

Conflicts of interest

P. C. has received speaker fees from Chiesi and Vertex Pharmaceutical. P. C. is funded by the Dutch Research Council (NWO-Veni) and Horizon EIC Pathfinder.

J. N. is an Industry Employee of Envisionit Deep (UK), a company that uses AI as a clinical decision support tool in medical imaging diagnosis. J.N. is also the director of Paeds Diagnostic Imaging and J Naidoo Inc. J. N. did not receive financial or research support from the companies for this article and the views expressed are those of the author and not of Envisionit Deep AI, Paeds Diagnostic Imaging or J Naidoo Inc.

S. C. S. is funded by an NIHR Advanced Fellowship Award (NIHR-301322). This article presents independent research funded by the National Institute for Health and Care Research (NIHR) and supported by the Great Ormond Street Hospital Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. E.P. is funded by the Royal Marsden Cancer Charity.

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Ciet, P., Eade, C., Ho, ML. et al. The unintended consequences of artificial intelligence in paediatric radiology. Pediatr Radiol 54, 585–593 (2024). https://doi.org/10.1007/s00247-023-05746-y

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