The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World
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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Need for cognition is an important factor influencing acceptance of and trust in decision support systems.
“The concept of the need for cognition has been identified in various studies as an important factor influencing the acceptance of and trust in decision support systems [ 10 , 11 , 21 ].”
From Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists (2025) · cites this paper as [10]
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References 102 · Cited by 57
- Authors
- Felmingham, C. M., Adler, N. R., Ge, Z., Morton, R. L., Janda, M., & Mar, V. J.
- Affiliations
- The Alfred Hospital (AU)
- Monash University (AU)
- The University of Sydney (AU)
- The University of Queensland (AU)
- Corresponding author
- Felmingham, Claire M.
- ORCID
- Venue
- American Journal of Clinical Dermatology
- Published
- 2020-12-22
- Volume
- 22
- Issue
- 2
- Pages
- 233-242
- ISSN
- 1175-0561 (ISSN-L)
- 1179-1888
- OpenAlex
- W3113456870
- Cited by
- 57
- References
- 102
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 19 · 2023: 9 · 2022: 10 · 2021: 4
- FWCI
- 0.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Clinical Dermatology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Health and Medical Research Council (APP1194703, Early Career Fellowship)
- Monash University (Research Training Program Scholarship)
- University of Sydney (Robinson Fellowship)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, skin cancer diagnosis, human factors, clinician-AI collaboration, clinical implementation of AI, cognitive biases, cognitive errors, skin lesion diagnosis
- MeSH terms
- Machine Learning, Dermatologists, Attitude of Health Personnel, Clinical Competence, Diagnostic Errors, Humans, Image Interpretation, Computer-Assisted, Man-Machine Systems, Skin, Skin Neoplasms
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