ArticleScientific data2023
A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 26 citations in OpenAlex.
- Demographic Reporting and the Absence of Hispanic/Latino Representation in Dermatology Artificial Intelligence: A Scoping Review.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- DermNet: integrative CNN-ViT architecture for bias mitigation in dermatological diagnostics using advanced unsupervised lesion segmentation.Scientific reports · 2026Article
- Improving Clinical Reliability of Diffusion-Based Dermatology Image Synthesis Through Synthetic Note Alignment and Post-Generation Filtering.IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems · 2026Article
- A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.BMC medical research methodology · 2025Article
- Planet-wide performance of a skin disease AI algorithm validated in Korea.NPJ digital medicine · 2025Article
- An Ingeniously Designed Skin Lesion Classification Model Across Clinical and Dermatoscopic Datasets.Diagnostics (Basel, Switzerland) · 2025Article
- Combining Real and Synthetic Data to Overcome Limited Training Datasets in Multimodal Learning.medRxiv : the preprint server for health sciences · 2025Article
- DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses.Scientific data · 2024Article
- The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.Scientific data · 2024Article
- Transparent medical image AI via an image-text foundation model grounded in medical literature.Nature medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients.
Indexed as
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.