Evidence map›Paper›PMID 37901399›Full record

ReviewFrontiers in medicine2023

Principles, applications, and future of artificial intelligence in dermatology.

Jesutofunmi A Omiye, Haiwen Gui, Roxana Daneshjou, Zhuo Ran Cai, Vijaytha Muralidharan

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
–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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.JID innovations : skin science from molecules to population health · 2026
    Review
  4. Article
  5. Skin on Drugs: Psychotropic Compounds in Cutaneous Biology.International journal of molecular sciences · 2026
    Review
  6. Review
  7. Article
  8. DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.Proceedings of SPIE--the International Society for Optical Engineering · 2026
    Article
  9. Review
  10. Review
  11. Integrative Machine Learning of Genetic and Lifestyle Factors for Personalized Skin Health.IEEE journal of translational engineering in health and medicine · 2026
    Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Article
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

5 authors.

Jesutofunmi A OmiyeDepartment of Dermatology, Stanford University, Stanford, CA, United States.
Haiwen GuiDepartment of Dermatology, Stanford University, Stanford, CA, United States.
Roxana DaneshjouDepartment of Dermatology, Stanford University, Stanford, CA, United States.
Zhuo Ran CaiDepartment of Dermatology, Stanford University, Stanford, CA, United States.
Vijaytha MuralidharanDepartment of Dermatology, Stanford University, Stanford, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.

Indexed as

artificial intelligence (AI)dermatologyfederated learninglarge language models (LLM)machine learningmelanoma

Identifiers

PMID37901399
PMCPMC10602645

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.