Evidence map›Paper›PMID 40463698›Full record

ArticleArXiv2025

Artificial intelligence-enabled precision medicine for inflammatory skin diseases.

Alice Tang, Maria L Wei, Anna Haemel, Cindy La, Marina Sirota, Ernest Y Lee

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Alice TangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0003-4745-0714
Maria L WeiDepartment of Dermatology, University of California, San Francisco, San Francisco, California, USA.ORCID 0000-0002-3568-1921
Anna HaemelDepartment of Dermatology, University of California, San Francisco, San Francisco, California, USA.
Cindy LaDepartment of Internal Medicine, Southern California Permanente Medical Group, Baldwin Park, California, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-7246-6083
Ernest Y LeeBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0001-5144-2552

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007618 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANDERSON, MARK S · 1985 to 2020
$29.6M
RESOURCE-BASED CENTER FOR THE ADVANCEMENT OF PRECISION MEDICINE IN RHEUMATOLOGYP30AR070155 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Mary C Nakamura · 2016 to 2026
$8.2M
UCSF Dermatology Training GrantT32AR007175 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Wilson Liao, Michael David Rosenblum · 1986 to 2026
$6.1M
Leveraging Clinical Data for Phenotyping and Predictive Modelling of Alzheimer’s DiseaseF30AG079504 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TANG, ALICE SUMMER · 2022 to 2025
$187k
NIAMS NIH HHS P30 AR070155NIAMS NIH HHS T32 AR007175NIA NIH HHS F30 AG079504NIGMS NIH HHS T32 GM007618
6 · The paper itself

Abstract

Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.

Identifiers

PMID40463698
PMCPMC12133082

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.