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Applications of Large Language Models in Pathology.
1Department of Pathology, University of Michigan, Ann Arbor, MI 48105, USA.
Large language models (LLMs) offer transformative potential in pathology by assisting with tasks like image interpretation and data extraction. However, careful integration and verification are crucial due to risks of errors and over-reliance.
Area of Science:
- Artificial Intelligence
- Computational Pathology
- Machine Learning
Background:
- Large language models (LLMs) are advanced neural networks capable of generating human-like text.
- These models are increasingly being explored for applications in various scientific fields, including medicine.
Purpose of the Study:
- To provide a historical overview of large language models.
- To highlight current and potential use cases of LLMs in the practice and education of pathology.
- To discuss the benefits and limitations of integrating LLMs into pathology workflows.
Main Methods:
- Review of existing literature on large language models.
- Analysis of transformer-based neural network architectures.
- Exploration of current and emerging applications in the field of pathology.
Main Results:
- LLMs can generate educational content, summarize text, extract data, and write code.
- Combined with vision models, LLMs can aid in histopathology image interpretation.
- Potential applications include assisting in case sign-out and report generation.
Conclusions:
- Large language models hold significant promise for revolutionizing pathology practice and education.
- Users must exercise caution, as LLMs can produce inaccuracies and hallucinations.
- Verification of AI-generated content and mindful integration are essential to mitigate risks like de-skilling and automation bias.

