Artificial intelligence: revolutionizing cardiology with large language models

Abstract

Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.

Exploring the role of Large Language Models (LLMs) in hematology: a systematic review of applications, benefits, and limitations

“…Additionally, there are many more tasks where LLMs could make significant contributions including, identifying adverse events, enriching risk prediction models, enhancing patient management, as well as improving administrative processes and compliance to guidelines. 26…”

“…Additionally, there are many more tasks where LLMs could make significant contributions including, identifying adverse events, enriching risk prediction models, enhancing patient management, as well as improving administrative processes and compliance to guidelines. 26 To harness the benefits of LLMs while mitigating the risks, future research should focus on enhancing the transparency and accountability of these models. Developing standards for the ethical use of AI in medicine, improving the diversity and reliability of training datasets, and implementing robust validation processes are critical steps toward responsible integration.…”

Machine learning in cardiac surgery: a narrative review

“…Moreover, existing ML risk models rely primarily on structured EHR data (labs, medications, vitals). Advances in natural language processing through large language models present a promising opportunity to enrich model inputs through incorporation of unstructured data (clinical notes, radiology reports) ( 54 ). Prospective clinical implementation will require a multidisciplinary collaboration between data scientists, healthcare informaticists, and clinicians to overcome these challenges.…”

Reply to Letter to the Editor: “Large Language Models: Could They Be the Next Generation of Clinical Decision Support Systems in Cardiovascular Diseases?”

“…In clinical settings, AI-driven LLMs enhance diagnostic accuracy and treatment efficacy by analyzing patient data, medical histories, and diagnostic images, providing tailored insights and recommendations to improve patient outcomes. 3 …”

“…Ethical considerations underscore the need for robust governance frameworks and interdisciplinary collaboration in healthcare. 3 …”