ReviewNPJ digital medicine2024
Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 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
29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Evaluating the Performance of Artificial Intelligence in Accurately Detecting Skin Cancer: An Umbrella Review of Systematic Reviews and Meta-analyses.American journal of clinical dermatology · 2026Pooled it
- Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis.The British journal of dermatology · 2025Pooled it
- Natural language processing in dermatology: A systematic literature review and state of the art.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024Pooled it
- Health Care Professionals' Perceptions of AI in Clinical Practice: Productivity, Enjoyment, and Pay.JMIR formative research · 2026Article
- The Evolving Role of Artificial Intelligence in Dermatology: A Meta-Analysis of Diagnostic Performance, Clinical Applications, and Implementation Challenges (2003-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Demonstrating the real-world impact of an artificial intelligence tool in radiology: are we focusing our efforts in the proper direction?European radiology · 2026Article
- Deep learning-based classification of gh-secreting pituitary neuroendocrine tumors granulation patterns using H&E-stained histopathology.BMC endocrine disorders · 2026Article
- How good are artificial intelligence tools at identifying benign skin lesions? A systematic review and meta-analysis of the specificity of artificial intelligence tools in diagnosing suspicious skin lesions.Skin health and disease · 2026Review
- When AI and Experts Agree on Error: Intrinsic Ambiguity in Dermatoscopic Images.Journal of imaging · 2026Article
- Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.NPJ digital medicine · 2026Article
- Factors influencing the effectiveness of artificial intelligence-assisted decision-making in medicine: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Patient Perspectives on the Use of Artificial Intelligence in Malignant Melanoma Diagnosis.Dermatology practical & conceptual · 2026Article
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A novel multi-agent spatiotemporal fusion framework for intelligent skin cancer diagnosis.Frontiers in oncology · 2026Article
- Effect of race and sex on lupus diagnosis in primary care: A randomized factorial survey study.PloS one · 2026Article
- Diagnostic Accuracy of Artificial Intelligence Applications on a Diverse Skin Image Set.Cureus · 2026Article
- Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema.Scientific reports · 2025Article
- Integrating Anatomical Site Information Into Vision Transformer Models for Skin Cancer Classification.Annals of dermatology · 2025Article
- The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.Nature medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 7 institutions in 3 countries.
Funding
Abstract
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
Identifiers
What 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.