The TRIPOD-LLM reporting guideline for studies using large language models is a research paper published in Nature Medicine (2025). On theSindex it has a DataRank of 0. It has been cited 389 times.
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NIH HHS
Grant: OT2 OD032701
NIBIB NIH HHS
Grant: R01 EB017205
NCATS NIH HHS
Grant: UL1 TR001445
NCI NIH HHS
Grant: R01 CA294033
NHLBI NIH HHS
Grant: R01 HL167811
FIC NIH HHS
Grant: U54 TW012043
NCI NIH HHS
Grant: U54 CA274516
National Science Foundation (NSF)
Grant: 1928614 & #2129076
NHLBI NIH HHS
Grant: 75N92020C00008
NLM NIH HHS
Grant: R01 LM013486
NHLBI NIH HHS
Grant: 75N92020C00021
National Institutes of Health
Grant: 5R01CA294033-02
Informatics strategies to improve immune-related adverse event detection in cancer patients
National Science Foundation
Grant: 2129076
Co-Development of Telehealth, Remote Patient Monitoring, and AI-based Tools for Inclusive Technology-Facilitated Healthcare Work of the Future
UK Research and Innovation
Grant: EP/Y018516/1
Sample Size guidance for developing and validating reliable and fair AI PREDICTion models in healthcare (SS-PREDICT)
National Institutes of Health
Grant: 3UL1TR001445-03S1
Clinical and Translational Science Award
National Institutes of Health
Grant: 3R01EB017205-06S1
Critical Care Informatics: Ethical considerations around the use and sharing of health-related data
National Institutes of Health
Grant: 3OT2OD032701-01S3
Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI
National Institutes of Health
Grant: 5U54TW012043-05
MUST Data Science Research Hub (MUDSReH)
National Science Foundation
Grant: 1928614
FW-HTF-RL: Collaborative Research: Future expert work in the age of "black box", data-intensive, and algorithmically augmented healthcare
National Institutes of Health
Grant: 5R01HL167811-02
Opportunistic Screening for ASCVD using a Multimodal Deep Learning Risk Prediction Model
FWCI
279.10
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals