ReviewJID innovations : skin science from molecules to population health2026
Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.
Review in JID innovations : skin science from molecules to population health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is emerging as a transformative and rapidly developing technology, with growing implications for the healthcare sector, including dermatology. We conducted a literature review using PubMed and EMBASE. Included studies primarily focused on the application of artificial intelligence for image-based classification. These artificial intelligence systems showed remarkable performance in controlled settings, even matching dermatologist-level accuracy for specific narrow tasks. Several common limitations were identified, including restricted dataset sizes, limited diagnostic diversity, potential selection bias, and inconsistencies in model evaluation. Clinical implementation should require careful attention to validation rigor, dataset diversity, implementation strategies, ethical considerations, and evidence of real-world impact.
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.