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Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know

  • Artificial intelligence in pediatric radiology
  • Published:
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Abstract

There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has the potential to improve clinical practice is welcomed, the level of detail included in early AI research reporting has been highly variable and inconsistent, particularly when compared to more traditional clinical research. However, inclusion checklists are now commonly available and accessible to those writing or reviewing clinical research papers. AI-specific reporting guidelines also exist and include distinct requirements, but these can be daunting for radiologists new to the field. Given that pediatric radiology is a specialty faced with workforce shortages and an ever-increasing workload, AI could help by offering solutions to time-consuming tasks, thereby improving workflow efficiency and democratizing access to specialist opinion. As a result, pediatric radiologists are expected to be increasingly leading and contributing to AI imaging research, and researchers and clinicians alike should feel confident that the findings reported are presented in a transparent way, with sufficient detail to understand how they apply to wider clinical practice. In this review, we describe two of the most clinically relevant and available reporting guidelines to help increase awareness and engage the pediatric radiologist in conducting AI imaging research. This guide should also be useful for those reading and reviewing AI imaging research and as a checklist with examples of what to expect.

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Acknowledgments

O.J.A. is funded by a National Institute for Health Research (NIHR) Career Development Fellowship (NIHR-CDF-2017-10-037). This article presents independent funded research — the views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, Medical Research Council, Royal College of Radiologists or the UK Department of Health.

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Authors and Affiliations

  1. Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1N 3JH, UK

    Riwa Meshaka, Owen J. Arthurs & Susan C. Shelmerdine

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

    Riwa Meshaka, Owen J. Arthurs, Neil J. Sebire & Susan C. Shelmerdine

  3. Great Ormond Street Hospital NIHR Biomedical Research Centre, London, UK

    Riwa Meshaka, Owen J. Arthurs, Neil J. Sebire & Susan C. Shelmerdine

  4. Department of Radiology, University Hospital Cologne, Cologne, Germany

    Daniel Pinto Dos Santos

  5. Department of Pathology, Great Ormond Street Hospital for Children, London, UK

    Neil J. Sebire

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

    Susan C. Shelmerdine

Authors
  1. Riwa Meshaka
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  2. Daniel Pinto Dos Santos
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  3. Owen J. Arthurs
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  4. Neil J. Sebire
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  5. Susan C. Shelmerdine
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Correspondence to Susan C. Shelmerdine.

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Meshaka, R., Pinto Dos Santos, D., Arthurs, O.J. et al. Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know. Pediatr Radiol 52, 2101–2110 (2022). https://doi.org/10.1007/s00247-021-05129-1

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  • DOI: https://doi.org/10.1007/s00247-021-05129-1

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