Large language models are gaining increasing interest in the medical community; however, an important but overlooked aspect of their capacity is their ability to integrate with tools. This integration greatly extends their potential application in health care.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
27,99 € / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
209,00 € per year
only 17,42 € per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Tran, B. X. et al. Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J. Clin. Med. 8, 360 (2019).
Yin, J., Ngiam, K. Y. & Teo, H. H. Role of artificial intelligence applications in real-life clinical practice: systematic review. J. Med. Internet Res. 23, e25759 (2021).
OpenAI et al. GPT-4 technical report. Preprint at https://doi.org/10.48550/arXiv.2303.08774 (2024).
Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).
Clusmann, J. et al. The future landscape of large language models in medicine. Commun. Med. 3, 141 (2023).
Rabilloud, N. et al. Deep learning methodologies applied to digital pathology in prostate cancer: a systematic review. Diagnostics 13, 2676 (2023).
Jiang, A. Q. et al. Mixtral of experts. Preprint at http://arxiv.org/abs/2401.04088 (2024).
Liévin, V., Hother, C. E., Motzfeldt, A. G. & Winther, O. Can large language models reason about medical questions? Patterns 5, 100943 (2024).
Schick, T. et al. Toolformer: language models can teach themselves to use tools. Preprint at https://doi.org/10.48550/arXiv.2302.04761 (2023).
Shen, Y. et al. HuggingGPT: solving AI tasks with ChatGPT and its friends in hugging face. Preprint at https://doi.org/10.48550/arXiv.2303.17580 (2023).
Roobol, M. J. et al. A calculator for prostate cancer risk 4 years after an initially negative screen: findings from ERSPC Rotterdam. Eur. Urol. 63, 627–633 (2013).
Nordström, T. et al. Prostate cancer screening using a combination of risk-prediction, MRI, and targeted prostate biopsies (STHLM3-MRI): a prospective, population-based, randomised, open-label, non-inferiority trial. Lancet Oncol. 22, 1240–1249 (2021).
Grönberg, H. et al. Prostate cancer screening in men aged 50-69 years (STHLM3): a prospective population-based diagnostic study. Lancet Oncol. 16, 1667–1676 (2015).
Prostate cancer: National care program - Short version for general practitioners [Swedish]. Regionala Cancercentrum https://cancercentrum.se/globalassets/cancerdiagnoser/prostatacancer/vardprogram/nationellt-vardprogram-kortyversion-allmanlakare-prostatacancer.pdf (2024).
Olsson, H. et al. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction. Nat. Commun. 13, 7761 (2022).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Supplementary information
Rights and permissions
About this article
Cite this article
Szolnoky, K., Nordström, T. & Eklund, M. Tomorrow’s patient management: LLMs empowered by external tools. Nat Rev Urol (2024). https://doi.org/10.1038/s41585-024-00965-w
Published:
DOI: https://doi.org/10.1038/s41585-024-00965-w