Large language models for infectious diseases require evidence generation and regulation.

Journal: Internal medicine journal
Published Date:

Abstract

Large language models (LLMs) offer significant potential in healthcare, especially in the Australian infectious diseases (ID) context, where a great deal of information must be gathered and synthesised. To maximise benefits, the use of evidence-based medicine principles, robust trials, thorough regulatory frameworks and timely guidelines statements are necessary. Additionally, proactive strategies utilising artificial intelligence architectures such as retrieval-augmented generation can help minimise risks, while optimising the benefits of LLM in ID.

Authors

  • Christina Gao
    Faculty of Health & Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
  • Shirajh Satheakeerthy
    Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia.
  • Christina Guo
    The Institute of Cancer Research, Sutton, London, United Kingdom.
  • Alyssa Pradhan
    The Alfred Hospital, Melbourne, VIC, 3004, Australia.
  • Andrew E C Booth
    SA Health, Adelaide, SA, 5000, Australia.
  • Weng Onn Chan
    Faculty of Health & Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.
  • Sanjat Kanjilal
    Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts.
  • Matthew Blake Roberts
    Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.
  • Camille Kotton
    Massachusetts General Hospital, Boston, MA, 02114, USA; Harvard Medical School, Boston, MA, 02115, USA.
  • Stephen Bacchi
    Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA 5000 Australia.

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