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  • Clinical Outlook
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Tomorrow’s patient management: LLMs empowered by external tools

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.

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References

  1. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  2. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  3. OpenAI et al. GPT-4 technical report. Preprint at https://doi.org/10.48550/arXiv.2303.08774 (2024).

  4. Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).

    Article  CAS  PubMed  Google Scholar 

  5. Clusmann, J. et al. The future landscape of large language models in medicine. Commun. Med. 3, 141 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rabilloud, N. et al. Deep learning methodologies applied to digital pathology in prostate cancer: a systematic review. Diagnostics 13, 2676 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Jiang, A. Q. et al. Mixtral of experts. Preprint at http://arxiv.org/abs/2401.04088 (2024).

  8. Liévin, V., Hother, C. E., Motzfeldt, A. G. & Winther, O. Can large language models reason about medical questions? Patterns 5, 100943 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Schick, T. et al. Toolformer: language models can teach themselves to use tools. Preprint at https://doi.org/10.48550/arXiv.2302.04761 (2023).

  10. 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).

  11. 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).

    Article  PubMed  Google Scholar 

  12. 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).

    Article  PubMed  Google Scholar 

  13. 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).

    Article  PubMed  Google Scholar 

  14. 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).

  15. Olsson, H. et al. Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction. Nat. Commun. 13, 7761 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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Correspondence to Martin Eklund.

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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

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