Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems

Formatted

BibTeX

@article{pike2025answering,
  title = {Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems},
  author = {Low, YS and Jackson, ML and Hyde, RJ and Brown, RE and Sanghavi, NM and Baldwin, JD and Pike, CW and Muralidharan, J and Hui, G and Alexander, N and Hassan, H and Nene, RV and Pike, M and Pokrzywa, CJ and Vedak, S and Yan, AP and Yao, DH and Zipursky, AR and Dinh, C and Ballentine, P and Derieg, DC and Polony, V and Chawdry, RN and Davies, J and Hyde, BB and Shah, NH and Gombar, S},
  journal = {Digital Health},
  year = {2025},
  volume = {11},
  pages = {20552076251348850},
  doi = {10.1177/20552076251348850},
}

RIS

TY  - JOUR
AU  - Low, YS
AU  - Jackson, ML
AU  - Hyde, RJ
AU  - Brown, RE
AU  - Sanghavi, NM
AU  - Baldwin, JD
AU  - Pike, CW
AU  - Muralidharan, J
AU  - Hui, G
AU  - Alexander, N
AU  - Hassan, H
AU  - Nene, RV
AU  - Pike, M
AU  - Pokrzywa, CJ
AU  - Vedak, S
AU  - Yan, AP
AU  - Yao, DH
AU  - Zipursky, AR
AU  - Dinh, C
AU  - Ballentine, P
AU  - Derieg, DC
AU  - Polony, V
AU  - Chawdry, RN
AU  - Davies, J
AU  - Hyde, BB
AU  - Shah, NH
AU  - Gombar, S
TI  - Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems
JO  - Digital Health
PY  - 2025
VL  - 11
SP  - 20552076251348850
DO  - 10.1177/20552076251348850
ER  -