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Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?
Aditya K Gupta1,2, Vasiliki Economopoulos2
1Division of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
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.
Insights
Artificial intelligence (AI) offers new tools to combat rising superficial fungal infections and antifungal resistance. Understanding AI, including large language models (LLMs), is crucial for improving dermatological care access and patient outcomes.
Area of Science:
- Dermatology
- Medical Informatics
- Artificial Intelligence
Background:
- Superficial fungal infections cause significant physical and psychological distress.
- Increasing prevalence and antifungal resistance necessitate novel therapeutic strategies.
- Gaps in dermatological care access highlight the need for innovative solutions.
Purpose of the Study:
- To review the fundamental principles of artificial intelligence (AI) and large language models (LLMs).
- To explore the potential applications and limitations of AI in managing superficial fungal infections.
- To discuss the current and future outlook of AI in dermatology.
Main Methods:
- Literature review of AI and LLM capabilities.
- Analysis of AI applications in clinical and laboratory settings.
- Discussion of AI's role in addressing dermatological care gaps.
Main Results:
- AI, including LLMs like ChatGPT, Claude, and Gemini, demonstrates advanced problem-solving and reasoning abilities.
- AI tools are being developed for both clinical and laboratory use in dermatology.
- Understanding AI mechanisms is essential for its safe and effective implementation.
Conclusions:
- AI presents a promising solution for improving access to dermatological care and patient outcomes.
- Further research and understanding of AI are vital for its successful integration into fungal infection management.
- AI holds significant potential to transform the landscape of superficial fungal infection treatment.
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