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The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World

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Abstract

Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. However, in the real-world clinical setting, the clinician will work in collaboration with AI. Existing evidence regarding the integration of such AI diagnostic tools into clinical practice is limited. Human factors, such as cognitive style, personality, experience, preferences, and attitudes may influence clinicians’ use of AI. In this review, we consider these human factors and the potential cognitive errors, biases, and unintended consequences that could arise when using an AI skin cancer diagnostic tool in the real world. Integrating this knowledge in the design and implementation of AI technology will assist in ensuring that the end product can be used effectively. Dermatologist leadership in the development of these tools will further improve their clinical relevance and safety.

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Authors and Affiliations

  1. School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia

    Claire M. Felmingham, Nikki R. Adler & Victoria J. Mar

  2. Victorian Melanoma Service, Alfred Hospital, 55 Commercial Road, Melbourne, VIC, 3004, Australia

    Claire M. Felmingham & Victoria J. Mar

  3. Monash eResearch Centre, Monash University, Clayton, Australia

    Zongyuan Ge

  4. Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Melbourne, VIC, Australia

    Zongyuan Ge

  5. Monash-Airdoc Research Centre, Monash University, Melbourne, VIC, Australia

    Zongyuan Ge

  6. NHMRC Clinical Trials Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia

    Rachael L. Morton

  7. Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia

    Monika Janda

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  1. Claire M. Felmingham
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  2. Nikki R. Adler
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Correspondence to Claire M. Felmingham.

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CMF is supported by a Monash University Research Training Program Scholarship. RLM is supported by a National Health and Medical Research Centre Investigator grant APP1194703 and a University of Sydney Robinson Fellowship. VJM is supported by a National Health and Medical Research Centre Early Career Fellowship. NRA, ZG, and MK, have no conflicts of interest that are directly relevant to the content of this article.

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CMF: conceptualisation, writing original draft. NRA: conceptualisation, reviewing and editing manuscript. ZG: reviewing and editing manuscript. RLM: reviewing and editing manuscript. MJ: conceptualisation, reviewing and editing manuscript. VJM: conceptualisation, reviewing and editing manuscript. All authors approved the final submitted version of the manuscript.

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Felmingham, C.M., Adler, N.R., Ge, Z. et al. The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World. Am J Clin Dermatol 22, 233–242 (2021). https://doi.org/10.1007/s40257-020-00574-4

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