[go: up one dir, main page]

Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Charting the future of cardiology with large language model artificial intelligence

Large language models represent a transformative leap in artificial intelligence and natural language processing, offering exciting potential across cardiology, from clinical care to education and research. However, several crucial challenges limit the practical implementation of large language models in cardiology. Interdisciplinary research is imperative to overcome these barriers.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Potential applications of LLMs in cardiology.

References

  1. Agrawal, M., Hegselmann, S., Lang, H., Kim, Y. & Sontag, D. Large language models are few-shot clinical information extractors. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing https://doi.org/10.18653/v1/2022.emnlp-main.130 (2022).

    Article  Google Scholar 

  2. Haberle, T. et al. The impact of nuance DAX ambient listening AI documentation: a cohort study. J. Am. Med. Inform. Assoc. 31, 975–979 (2024).

    Article  PubMed  Google Scholar 

  3. Itelman, E., Witberg, G. & Kornowski, R. AI-assisted clinical decision making in interventional cardiology: the potential of commercially available large language models. JACC Cardiovasc. Interv. 17, 1858–1860 (2024).

    Article  PubMed  Google Scholar 

  4. Cunningham, J. W. et al. Natural language processing for adjudication of heart failure in a multicenter clinical trial: a secondary analysis of a randomized clinical trial. JAMA Cardiol. 9, 174–181 (2024).

    Article  PubMed  Google Scholar 

  5. Han, C. et al. Evaluation of GPT-4 for 10-year cardiovascular risk prediction: insights from the UK Biobank and KoGES data. iScience 27, 109022 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Oh, J., Lee, G., Bae, S., Kwon, J. & Choi, E. ECG-QA: a comprehensive question answering dataset combined with electrocardiogram. Proceedings of the 37th International Conference on Neural Information Processing Systems 66277–66288 (Curran, 2024).

  7. Kozaily, E. et al. Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients’ questions about heart failure. Int. J. Cardiol. 408, 132115 (2024).

    Article  PubMed  Google Scholar 

  8. Sarraju, A. et al. Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model. J. Am. Med. Assoc. 329, 842–844 (2023).

    Article  Google Scholar 

  9. Kangiszer, G. et al. Low performance of ChatGPT on echocardiography board review questions. JACC Cardiovasc. Imaging 17, 330–332 (2024).

    Article  PubMed  Google Scholar 

  10. Inam, M. et al. A review of top cardiology and cardiovascular medicine journal guidelines regarding the use of generative artificial intelligence tools in scientific writing. Curr. Probl. Cardiol. 49, 102387 (2024).

    Article  PubMed  Google Scholar 

  11. Tayebi Arasteh, S. et al. Large language models streamline automated machine learning for clinical studies. Nat. Commun. 15, 1603 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Unlu, O. et al. Retrieval-augmented generation–enabled GPT-4 for clinical trial screening. NEJM AI 1, AIoa2400181 (2024).

    Article  Google Scholar 

  13. Zack, T. et al. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. Lancet Digit. Health 6, e12–e22 (2024).

    Article  PubMed  CAS  Google Scholar 

  14. Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

    Article  PubMed  CAS  Google Scholar 

  15. Zakka, C. et al. Almanac — retrieval-augmented language models for clinical medicine. NEJM AI 1, AIoa2300068 (2024).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramsey M. Wehbe.

Ethics declarations

Competing interests

The author is a consultant for GE Healthcare, has investments in Microsoft, and has received research support from the American Society of Nuclear Cardiology and Pfizer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wehbe, R.M. Charting the future of cardiology with large language model artificial intelligence. Nat Rev Cardiol (2024). https://doi.org/10.1038/s41569-024-01105-y

Download citation

  • Published:

  • DOI: https://doi.org/10.1038/s41569-024-01105-y

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing