Evidence map›Paper›PMID 40860311›Full record

ReviewClinical, cosmetic and investigational dermatology2025

Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?

Aditya K Gupta, Vasiliki Economopoulos

Abstract readReview
In one paragraph

Review in Clinical, cosmetic and investigational dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

2 authors.

Aditya K GuptaDivision of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-8664-7723
Vasiliki EconomopoulosMediprobe Research Inc., London, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Superficial fungal infections can have significant physical and psychological consequences for affected patients. These painful infections have become more prevalent and the rise of antifungal resistant strains is of great concern. New tools in the fight against these infections are needed, especially in areas were appropriate dermatological care is lacking. Artificial intelligence (AI) offers a potential solution for these care gaps. AI's capabilities have been increasing in sophistication at an astonishing pace, with large language models (LLMs), such as ChatGPT (OpenAI), Claude (Anthropic) and Gemini (Google) being capable of generating detailed responses to complex problems as well as demonstrating reasoning type behaviour. AI is currently in use and being developed for use within the clinic as well as the laboratory, with the potential to significantly improve access to dermatological care and patient outcomes. However, understanding how these AI models work at a basic level is necessary for safe, effective and efficient use and application to the management of superficial fungal infections. In this review, we provide a high-level description of how these models work, discuss the potentials and pitfalls of AI and LLMs, as well as their applications and the current and future outlook for the field.

Indexed as

artificial intelligence in dermatologydermatology specific LLMsethics of artificial intelligence in medicinesuperficial fungal infections

Identifiers

PMID40860311
PMCPMC12375310

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.