Skin and Syntax: Large Language Models in Dermatopathology
Open accessAbstract
This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes.
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LLMs are increasingly being explored beyond their traditional text-based applications.
“LLMs, a class of AI systems designed to process, interpret, and generate human-like text, are increasingly being explored beyond their traditional text-based applications [67].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [67]
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Cited by
Sorted by citations
Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature
Abu-Dayeh, A., Cazzato, G., & Giubellino, A. (2026) · Diagnostics · doi:10.3390/diagnostics16111702 · Cited by 1 · see citing works
“LLMs, a class of AI systems designed to process, interpret, and generate human-like text, are increasingly being explored beyond their traditional text-based applications [67].”
- Authors
- Shah, A., Wahood, S., Guermazi, D., Brem, C. E., & Saliba, E.
- Affiliations
- Brown University (US)
- Boston University (US)
- Corresponding author
- Saliba, Elie
- Venue
- Dermatopathology
- Publisher
- MDPI AG
- Published
- 2024-02-14
- Volume
- 11
- Issue
- 1
- Pages
- 101-111
- ISSN
- 2296-3529 (ISSN-L)
- PMC
- PMC10885095
- OpenAlex
- W4391810774
- Cited by
- 25
- References
- 32
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 7
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Dermatopathology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Keywords
- large language models, AI-assisted diagnosis, patient education, multimodal large language models, model interpretability, data quality, data privacy, algorithmic bias, human-AI collaboration
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