Skip to content
DraconiticResearch
Search Sources API
194,230 studies31 sourcesUpdated 38 min ago
PreprintAll rights reservedmedRxiv

AI-Empowered Multimodal Data Integration and Knowledge Discovery for Pharmacovigilance

12 more authors
  1. 1Mayo Clinic

Related

  1. Similarity-based profiling of pharmacovigilance data for drug safety pattern discovery
    Seongjae Park, SeongJin Wi, Heeseon Jo +3
    The Korean Journal of Physiology & Pharmacology : Official Journal of the Korean Physiological Society and the Korean Society of PharmacologyPeer-reviewedCC BY-NC 4.0Full textPharmacology & toxicologyPMC
  2. Are health sciences students prepared for pharmacovigilance? A nationwide study of knowledge and suspected adverse drug reaction reporting.
    Dorota Kopciuch, Julia Wach, Krzysztof Kus
    BMC medical educationPeer-reviewedCC BYFull textPharmacology & toxicologyEurope PMC
  3. Artificial Intelligence, Multi-Omics, and Multimodal Integration in Precision Radiotherapy for Head and Neck Cancer: From Biological Insights to Treatment Decisions
    Miao Rao, Yingjian Wang, Minglun Li +1
    BiomedicinesPeer-reviewedCC BYFull textGeneral medicineEurope PMC
  4. Artificial intelligence across the gastric cancer care continuum: Clinical evidence, multimodal integration, and translation
    Junsong Yang, Jin Tang, Jinsong He
    iSciencePeer-reviewedCC BY-NC 4.0Full textEpidemiology & public healthCell PressCrossrefEurope PMCEurope PMC+1
  5. Digital Twins in Oncology: From Multimodal Data Integration to Precision Clinical Practice.
    Annamalai Vairavan, Rupsa Bhattacharjee, Bagyam Raghavan
    The Indian journal of radiology & imagingPeer-reviewedCC BY-NC-NDFull textMedical imaging & radiologyEurope PMC
BibTeX
@misc{li2026aiempowered,
  title = {{AI-Empowered Multimodal Data Integration and Knowledge Discovery for Pharmacovigilance}},
  author = {Y. Li and H. Zhou and F.-Y. Yeh and Z. Sun and J. Zheng and S. Alzaidi and H. Guan and J. Li and D. L. Seger and L. Wang and M. Li and X. Li and T. Chen and J. M. Plasek and H. Li and A. Jiang and P. Hong and F. Li and W. Zhang and L. Cui and D. W. Bates and L. Zhou and Y. He and C. Tao},
  howpublished = {medRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.20.26365969},
  url = {https://www.medrxiv.org/content/10.64898/2026.10.20.26365969v1},
}
RIS
TY  - UNPB
TI  - AI-Empowered Multimodal Data Integration and Knowledge Discovery for Pharmacovigilance
AU  - Y. Li
AU  - H. Zhou
AU  - F.-Y. Yeh
AU  - Z. Sun
AU  - J. Zheng
AU  - S. Alzaidi
AU  - H. Guan
AU  - J. Li
AU  - D. L. Seger
AU  - L. Wang
AU  - M. Li
AU  - X. Li
AU  - T. Chen
AU  - J. M. Plasek
AU  - H. Li
AU  - A. Jiang
AU  - P. Hong
AU  - F. Li
AU  - W. Zhang
AU  - L. Cui
AU  - D. W. Bates
AU  - L. Zhou
AU  - Y. He
AU  - C. Tao
PY  - 2026
DA  - 2026/10/20
PB  - medRxiv
DO  - 10.64898/2026.10.20.26365969
UR  - https://www.medrxiv.org/content/10.64898/2026.10.20.26365969v1
ER  -