Artificial intelligence: revolutionizing cardiology with large language models.

Area of Science:

• Clinical Informatics
• Computational Linguistics
• Medical Artificial Intelligence

Background:

• Natural language processing (NLP) is increasingly vital in healthcare, impacting patient care, clinical workflows, and research.
• Existing NLP applications include automated clinical note generation, medical coding, patient/clinician chatbots, and clinical trial recruitment.
• A historical overview and technical background of NLP techniques are essential for understanding their evolution in medicine.

Purpose of the Study:

• To review the historical development of natural language processing techniques in clinical settings.
• To discuss implementation strategies for NLP tools, with a specific focus on large language models (LLMs).
• To explore future opportunities for NLP applications within the field of cardiology.

Main Methods:

• Literature review of natural language processing techniques and their historical progression.
• Analysis of implementation strategies for NLP tools in healthcare.
• Focused examination of large language models (LLMs) and their potential in clinical applications.

Main Results:

• NLP techniques have evolved significantly, offering diverse applications across patient care, administration, and research.
• Implementation strategies are crucial for the successful integration of NLP tools into clinical workflows.
• Large language models represent a significant advancement with substantial potential for healthcare innovation.

Conclusions:

• Natural language processing offers transformative potential for improving clinical care and medical research.
• Strategic implementation of NLP, particularly LLMs, is key to unlocking these benefits.
• Future research should focus on the specific applications and integration of NLP in cardiology.