Recognising errors in AI implementation in radiology: A narrative review

Affiliations

¹ Department of Midwifery & Radiography, C.R.R.A.G. Research Group, School of Health and Medical Sciences, City St George's, University of London, London, UK; Magnitiki Tomografia Kerkiras, Corfu, Greece; European Federation of Radiographer Societies (EFRS), Cumieira, Portugal. Electronic address: nikos.stogiannos@citystgeorges.ac.uk.
² Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
³ Department of Diagnostic and Interventional Neuroradiology, University Hospital Basel, Basel, Switzerland; Department of Pediatric Radiology, University Children's Hospital Basel, Basel, Switzerland.
⁴ Department of Radiology, University Medical Center Mainz, Mainz, Germany.
⁵ Hardian Health, United Kingdom.
⁶ Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.
⁷ Department of Radiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
⁸ Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
⁹ Department of Mathematics and Computer Science, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
¹⁰ Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom; University College London Great Ormond Street Institute of Child Health, London, United Kingdom; NIHR Great Ormond Street Hospital Biomedical Research Centre, Bloomsbury, London, United Kingdom.
¹¹ Romion Health, Utrecht, the Netherlands; Health AI Register, Utrecht, the Netherlands.
¹² Department of Radiotherapy and Data Science Center in Health, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
¹³ Artificial Intelligence and Translational Imaging (ATI) Laboratory, Department of Radiology, Medical School, University of Crete, Voutes, Heraklion, Crete, Greece; Division of Radiology, Department for Clinical Science Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden; Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Crete, Greece.
¹⁴ Department of Midwifery & Radiography, C.R.R.A.G. Research Group, School of Health and Medical Sciences, City St George's, University of London, London, UK; European Federation of Radiographer Societies (EFRS), Cumieira, Portugal; Department of Neuroimaging, King's College London, London, United Kingdom; European Society of Medical Imaging Informatics (EuSoMII), Vienna, Austria.

PMID: 40674942
DOI: 10.1016/j.ejrad.2025.112311

Free article

Review

Recognising errors in AI implementation in radiology: A narrative review

Nikolaos Stogiannos et al. Eur J Radiol. 2025 Oct.

Free article

. 2025 Oct:191:112311.

doi: 10.1016/j.ejrad.2025.112311. Epub 2025 Jul 10.

Authors

Affiliations

¹ Department of Midwifery & Radiography, C.R.R.A.G. Research Group, School of Health and Medical Sciences, City St George's, University of London, London, UK; Magnitiki Tomografia Kerkiras, Corfu, Greece; European Federation of Radiographer Societies (EFRS), Cumieira, Portugal. Electronic address: nikos.stogiannos@citystgeorges.ac.uk.
² Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
³ Department of Diagnostic and Interventional Neuroradiology, University Hospital Basel, Basel, Switzerland; Department of Pediatric Radiology, University Children's Hospital Basel, Basel, Switzerland.
⁴ Department of Radiology, University Medical Center Mainz, Mainz, Germany.
⁵ Hardian Health, United Kingdom.
⁶ Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.
⁷ Department of Radiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
⁸ Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
⁹ Department of Mathematics and Computer Science, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
¹⁰ Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom; University College London Great Ormond Street Institute of Child Health, London, United Kingdom; NIHR Great Ormond Street Hospital Biomedical Research Centre, Bloomsbury, London, United Kingdom.
¹¹ Romion Health, Utrecht, the Netherlands; Health AI Register, Utrecht, the Netherlands.
¹² Department of Radiotherapy and Data Science Center in Health, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
¹³ Artificial Intelligence and Translational Imaging (ATI) Laboratory, Department of Radiology, Medical School, University of Crete, Voutes, Heraklion, Crete, Greece; Division of Radiology, Department for Clinical Science Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden; Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Crete, Greece.
¹⁴ Department of Midwifery & Radiography, C.R.R.A.G. Research Group, School of Health and Medical Sciences, City St George's, University of London, London, UK; European Federation of Radiographer Societies (EFRS), Cumieira, Portugal; Department of Neuroimaging, King's College London, London, United Kingdom; European Society of Medical Imaging Informatics (EuSoMII), Vienna, Austria.

PMID: 40674942
DOI: 10.1016/j.ejrad.2025.112311

Abstract

The implementation of AI can suffer from a wide variety of failures. These failures can impact the performance of AI algorithms, impede the adoption of AI solutions in clinical practice, lead to workflow delays, or create unnecessary costs. This narrative review aims to comprehensively discuss different reasons for AI failures in Radiology through the analysis of published evidence across three main components of AI implementation: (i) the AI models throughout their lifecycle, (ii) the technical infrastructure, including the hardware and software needed to develop and deploy AI models and (iii) the human factors involved. Ultimately, based on the identified errors, this report aims to propose solutions to optimise the use and adoption of AI in radiology.

Keywords: Artificial Intelligence; Errors; Failures; Implementation; Radiology.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Editor Conflict of Interest Statement Daniel Pinto dos Santos, Michail Klontzas, Renato Cuocolo, and Burak Koçak, given their role as Section Editors, had no involvement in the peer-review of this article and has no access to information regarding its peer-review.

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Recognising errors in AI implementation in radiology: A narrative review

Affiliations

Recognising errors in AI implementation in radiology: A narrative review

Authors

Affiliations

Abstract

Conflict of interest statement

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources