Large language models for infectious diseases require evidence generation and regulation
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
- Alyssa Pradhan (ORCID: https://orcid.org/0000-0001-9499-0292)
- Camille N. Kotton (ORCID: https://orcid.org/0000-0001-7320-2234)
- Sanjat Kanjilal (ORCID: https://orcid.org/0000-0002-1221-5725)
- Andrew Booth
- Stephen Bacchi (ORCID: https://orcid.org/0000-0001-5130-8628)
- Christina Guo (ORCID: https://orcid.org/0000-0002-1645-3202)
- Matthew B. Roberts (ORCID: https://orcid.org/0000-0003-0869-6463)
- Christina Gao (ORCID: https://orcid.org/0009-0005-0033-3352)
- Weng Onn Chan (ORCID: https://orcid.org/0000-0003-2646-5677)
- Shirajh Satheakeerthy
Institutions
- Royal Adelaide Hospital (AU)
- Harvard University (US)
- Johns Hopkins University (US)
- Lyell McEwin Hospital (AU)
- The Alfred Hospital (AU)
- Massachusetts General Hospital (US)
- Zero to Three (US)
- The University of Adelaide (AU)
Publication Details
- Journal
- Internal Medicine Journal
- Published
- 2025-05-27
- DOI
- https://doi.org/10.1111/imj.70072
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65
Funders
- Fulbright Association