Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance

Jack Gallifant; Leo Anthony Celi; Elad Sharon; Danielle S Bitterman

doi:10.1200/CCI.24.00051

Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance

JCO Clin Cancer Inform. 2024 May:8:e2400051. doi: 10.1200/CCI.24.00051.

Authors

Jack Gallifant^{1

2}, Leo Anthony Celi^{1

3

4}, Elad Sharon⁵, Danielle S Bitterman^{6

7}

Affiliations

¹ Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA.
² Department of Critical Care, Guy's & St Thomas' NHS Trust, London, United Kingdom.
³ Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
⁴ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
⁵ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA.
⁶ Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.
⁷ Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA.

PMID: 38713889
DOI: 10.1200/CCI.24.00051

Abstract

This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance.

Publication types

Editorial

MeSH terms

Adverse Drug Reaction Reporting Systems
Artificial Intelligence*
Data Collection / methods
Electronic Health Records*
Humans
Medical Oncology* / methods
Natural Language Processing*
Neoplasms / drug therapy
Pharmacovigilance*