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AIM in Medical Education

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Artificial Intelligence in Medicine

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Abstract

Artificial intelligence (AI) is making a global impact on various professions ranging from commerce to healthcare. This section looks at how it is beginning and will continue to impact other areas such as medical education. The multifaceted yet socrato-didactic methods of education need to evolve to cater for the twenty-first-century medical educator and trainee. Advances in machine learning and artificial intelligence are paving the way to new discoveries in medical education delivery.

Methods

This chapter begins by introducing the broad concepts of AI that are relevant to medical education and then addresses some of the emerging technologies employed to directly cater for aspects of medical education methodology and innovations to streamline education delivery, education assessments, and education policy. It then builds on this to further explore the nature of new artificial intelligence concepts for medical education delivery, educational assessments, and clinical education research discovery in a PRISMA-guided systematic review and meta-analysis.

Results

Results from the meta-analysis showed improvement from using either AI alone or with conventional education methods compared to conventional methods alone. A significant pooled weighted mean difference ES estimate of ES 4.789; CI 1.9–7.67; p = 0.001, I2 = 93% suggests a 479% learner improvement across domains of accuracy, sensitivity to performing educational tasks, and specificity. Significant amount of bias between studies was identified and a model to reduce bias is proposed.

Conclusion

AI in medical education shows considerable promise in domains of improving learners’ outcomes; this chapter rounds off its discussion with the role of AI in simulation methodologies and performance assessments for medical education, highlighting areas where it could augment how we deliver training.

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Authors and Affiliations

  1. Hamlyn Centre for Robotics and Artificial Intelligence, Department of Surgery and Cancer, Imperial College London, London, UK

    Joseph Davids, Kyle Lam & Hutan Ashrafian

  2. Imperial College London NHS Trust, London, UK

    Joseph Davids, Kyle Lam & Amr Nimer

  3. National Hospital for Neurology and Neurosurgery Queen Square, London, UK

    Joseph Davids

  4. Institute of Global Health Innovation, Imperial College London, London, UK

    Hutan Ashrafian

  5. Department of Surgery and Cancer, Imperial College London NHS Trust, London, UK

    Hutan Ashrafian

  6. Hamlyn Centre for Robotic Surgery and AI, Department of Surgery and Cancer, Imperial College London, London, UK

    Stamatia Gianarrou

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  1. Joseph Davids
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  2. Kyle Lam
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  3. Amr Nimer
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  4. Stamatia Gianarrou
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  5. Hutan Ashrafian
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Correspondence to Joseph Davids .

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Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

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Davids, J., Lam, K., Nimer, A., Gianarrou, S., Ashrafian, H. (2022). AIM in Medical Education. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_30

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