Evidence map›Paper›PMID 41093473›Full record

ReviewDermatologic clinics2025

The Current State and Future Prospects for Artificial Intelligence in Dermatology.

Christopher J Thang, Caitlyn Duffy, Sara Khattab, Yevgeniy R Semenov

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Dermatologic clinics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Christopher J ThangDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Bartlett Hall 6R, Room 626, Boston, MA 02114, USA.
Caitlyn DuffyDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Bartlett Hall 6R, Room 626, Boston, MA 02114, USA.
Sara KhattabDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Bartlett Hall 6R, Room 626, Boston, MA 02114, USA.
Yevgeniy R SemenovDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Bartlett Hall 6R, Room 626, Boston, MA 02114, USA; Harvard Data Science Initiative, Harvard University, Boston, MA, USA. Electronic address: ysemenov@mgh.harvard.edu.

Funding

Predictive modeling of cutaneous immune checkpoint inhibitor toxicitiesK23AR080791 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Yevgeniy R Semenov · 2023 to 2026
$704k
NCI NIH HHS L30 CA264747NIAMS NIH HHS K23 AR080791
6 · The paper itself

Abstract

Recent developments in artificial intelligence (AI) have the potential to revolutionize dermatology by improving patient-care and reducing administrative burden. We discuss current and future applications of AI in dermatology that can enhance clinical decision-making, diagnostic and prognostic processes, and health care operations. We will explore AI-driven imaging tools for dermatologists and dermatopathologists, multimodal AI techniques enabling precision medicine, and generative AI systems that support clinical practice. While further refinements are needed for widespread implementation, AI-based applications present an opportunity for dermatologists to improve patient-care and minimize resource demands.

Indexed as

Artificial IntelligenceDermatologySkin DiseasesClinical Decision-MakingHumansPrecision MedicineArtificial intelligenceArtificial intelligence for dermatologyArtificial intelligence for health careDeep learningDermatopathologyMachine learningMedical dermatology

Identifiers

PMID41093473
PMCPMC13591430

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.