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Challenges and Potential of Artificial Intelligence in Neuroradiology

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Abstract

Purpose

Artificial intelligence (AI) has emerged as a transformative force in medical research and is garnering increased attention in the public consciousness. This represents a critical time period in which medical researchers, healthcare providers, insurers, regulatory agencies, and patients are all developing and shaping their beliefs and policies regarding the use of AI in the healthcare sector. The successful deployment of AI will require support from all these groups. This commentary proposes that widespread support for medical AI must be driven by clear and transparent scientific reporting, beginning at the earliest stages of scientific research.

Methods

A review of relevant guidelines and literature describing how scientific reporting plays a central role at key stages in the life cycle of an AI software product was conducted. To contextualize this principle within a specific medical domain, we discuss the current state of predictive tissue outcome modeling in acute ischemic stroke and the unique challenges presented therein.

Results and Conclusion

Translating AI methods from the research to the clinical domain is complicated by challenges related to model design and validation studies, medical product regulations, and healthcare providers’ reservations regarding AI’s efficacy and affordability. However, each of these limitations is also an opportunity for high-impact research that will help to accelerate the clinical adoption of state-of-the-art medical AI. In all cases, establishing and adhering to appropriate reporting standards is an important responsibility that is shared by all of the parties involved in the life cycle of a prospective AI software product.

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Funding

This work was supported by the Canada Research Chairs program as well as by the River Fund at Calgary Foundation.

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Authors and Affiliations

  1. Department of Radiology, University of Calgary, Calgary, Canada

    Anthony J. Winder, Emma AM Stanley & Nils D. Forkert

  2. Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

    Anthony J. Winder, Emma AM Stanley & Nils D. Forkert

  3. Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada

    Emma AM Stanley

  4. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada

    Emma AM Stanley & Nils D. Forkert

  5. Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Jens Fiehler

  6. Department of Clinical Neuroscience, University of Calgary, Calgary, Canada

    Nils D. Forkert

  7. Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada

    Nils D. Forkert

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  1. Anthony J. Winder
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  2. Emma AM Stanley
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Correspondence to Anthony J. Winder.

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Conflict of interest

J. Fiehler is, by appointment of the university hospital, the CEO of Eppdata GmbH and has received fees as consultant or lecturer from Acandis, Bayer, Boehringer-Ingelheim, Codman, Covidien, MicroVention, Penumbra, Philips, Sequent, Siemens and Stryker. Consultant for Codman, MicroVention. Lectures for Boehringer-Ingelheim, Covidien, and Penumbra. Funding to institution: MicroVention. Unrelated: consultant for Acandis, Sequent, Stryker. A.J. Winder, E.A. Stanley and N.D. Forkert declare that they have no competing interests.

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Winder, A.J., Stanley, E.A., Fiehler, J. et al. Challenges and Potential of Artificial Intelligence in Neuroradiology. Clin Neuroradiol 34, 293–305 (2024). https://doi.org/10.1007/s00062-024-01382-7

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