ArticleJAMA dermatology2021
Risk of Bias and Error From Data Sets Used for Dermatologic Artificial Intelligence.
Article in JAMA dermatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Robustness of a convolutional neural network trained on dermoscopic images and challenged with close-up images.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Article
- Quality and reliability of femoral neck fracture educational short videos: a cross-sectional study.Scientific reports · 2026Article
- Improving dataset transparency in dermatologic Artificial Intelligence using a dataset nutrition label.NPJ digital medicine · 2025Article
- MST-AI: Skin Color Estimation in Skin Cancer Datasets.Journal of imaging · 2025Article
- Pediatric Transplant and Cellular Therapy Consortium RESILIENT Conference on Pediatric Chronic Graft-Versus-Host Disease Survivorship After Hematopoietic Cell Transplantation: Part IV. Patient Important Outcomes.Transplantation and cellular therapy · 2025Article
- CORE-MD clinical risk score for regulatory evaluation of artificial intelligence-based medical device software.NPJ digital medicine · 2025Review
- The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective.Life (Basel, Switzerland) · 2024Article
- ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms.JAAD international · 2023Article
- Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential.JID innovations : skin science from molecules to population health · 2022Article
- Artificial intelligence technologies and compassion in healthcare: A systematic scoping review.Frontiers in psychology · 2022Article
- The State of Artificial Intelligence in Skin Cancer Publications.Journal of cutaneous medicine and surgeryArticle
- A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature.Ophthalmology scienceArticle
Corrections and comments
- Comment on
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
34550304What 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.