Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?
Bibliographic record
Abstract
Aditya K Gupta,1,2 Vasiliki Economopoulos2 1Division of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; 2Mediprobe Research Inc., London, Ontario, CanadaCorrespondence: Aditya K Gupta, Mediprobe Research Inc., 645 Windermere Road, London, Ontario, N5X 2P1, Canada, Tel +1-519-851-9715, Fax +1-519-657-4233, Email agupta@mediproberesearch.comAbstract: 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.Keywords: artificial intelligence in dermatology, superficial fungal infections, dermatology specific LLMs, ethics of artificial intelligence in medicine
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".