Large language models and rheumatology: are we there yet?

Area of Science:

• Medical Informatics
• Artificial Intelligence in Medicine
• Rheumatology

Background:

• Recent advancements in artificial intelligence have introduced large language models (LLMs) and multimodal models.
• These technologies present novel opportunities and challenges in medical research and clinical practice.
• Rheumatology, characterized by abundant data and complex decision-making, is a prime area for AI integration.

Purpose of the Study:

• To provide a comprehensive overview of the current state of large language models (LLMs) in rheumatology.
• To discuss the potential applications of LLMs in transforming rheumatologic care.
• To identify and address the challenges associated with implementing LLMs in rheumatology.

Main Methods:

• Literature review of recent developments in LLMs and their application in medicine.
• Analysis of the potential impact of LLMs on diagnostic procedures, patient interaction, and data management in rheumatology.
• Discussion of existing and anticipated challenges in the adoption of LLMs within the rheumatology field.

Main Results:

• LLMs show significant potential to enhance diagnostic accuracy and efficiency in rheumatology.
• These models can improve patient engagement and streamline data management processes.
• The integration of LLMs promises more personalized and efficient healthcare outcomes in rheumatology.

Conclusions:

• The advent of LLMs marks a transformative period for rheumatology.
• Addressing the challenges is crucial for harnessing the full potential of LLMs in rheumatology.
• LLMs are poised to significantly advance personalized and efficient patient care in rheumatology.