ReviewAmerican journal of clinical dermatology2021
The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.
Review in American journal of clinical dermatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Systematic Review on Artificial Intelligence in Rheumatology Practice: From Implementation Concerns to Imaging and Clinical Application.International journal of rheumatic diseases · 2026Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.BMJ (Clinical research ed.) · 2022Guideline
- Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.Nature medicine · 2022Guideline
- Comprehensive recommendations for the implementation of artificial intelligence in healthcare: a narrative review on facilitators and barriers.BMJ open quality · 2026Review
- Vocabulary at the Living-Machine Interface: A Narrative Review of Shared Lexicon for Hybrid AI.Biomimetics (Basel, Switzerland) · 2025Article
- Human performance evaluation of a pediatric artificial intelligence sepsis model.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.Neurology · 2025Article
- Mental health practitioners' perceptions and adoption intentions of AI-enabled technologies: an international mixed-methods study.BMC health services research · 2025Article
- Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists.Journal of medical Internet research · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Navigating the AI tide: challenges, opportunities, and future directions for early-career dermatologists.Frontiers in medicine · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey.Computational and structural biotechnology journal · 2024Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- Artificial intelligence and skin cancer.Frontiers in medicine · 2024Review
- Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform.JMIR human factors · 2023Article
- Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers.Current oncology (Toronto, Ont.) · 2023Article
- The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study.BMC primary care · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
33354741What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.