{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T05:25:26Z","timestamp":1794288326984,"version":"4.9.0"},"reference-count":112,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Research in Social and Administrative Pharmacy"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.sapharm.2026.09.001","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:41:44Z","timestamp":1790750504000},"page":"80-90","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integration of artificial intelligence into pharmacovigilance: A systematic review"],"prefix":"10.1016","volume":"23","author":[{"given":"Iman","family":"El Sayed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramez Naguib","family":"Bedwany","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mai","family":"Salama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Said Hassan","family":"Eltrass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.sapharm.2026.09.001_bib1","series-title":"The Importance of Pharmacovigilance","year":"2002"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib2","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1002\/cpt.1255","article-title":"Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing","volume":"105","author":"Schmider","year":"2019","journal-title":"Clin Pharmacol Ther"},{"key":"10.1016\/j.sapharm.2026.09.001_bib3","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s40264-020-01001-7","article-title":"Artificial intelligence, real-world automation and the safety of medicines","volume":"44","author":"Bate","year":"2021","journal-title":"Drug Saf"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s40290-022-00441-z","article-title":"The use of artificial intelligence in pharmacovigilance: a systematic review of the literature","volume":"36","author":"Salas","year":"2022","journal-title":"Pharmac Med"},{"key":"10.1016\/j.sapharm.2026.09.001_bib5","volume":"vol. 1","author":"Barr","year":"1979"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib7","first-page":"17","article-title":"Process analytical technology in the pharmaceutical industry: a toolkit for continuous improvement","volume":"60","author":"Scott","year":"2006","journal-title":"PDA J Pharm Sci Technol"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1111\/ced.14029","article-title":"Artificial intelligence, machine learning and deep learning: definitions and differences","volume":"45","author":"Jakhar","year":"2020","journal-title":"Clin Exp Dermatol"},{"key":"10.1016\/j.sapharm.2026.09.001_bib9","series-title":"Artificial Intelligence and Machine Learning Applications for Sustainable Development","author":"Singh","year":"2025"},{"issue":"9","key":"10.1016\/j.sapharm.2026.09.001_bib10","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.tips.2019.07.005","article-title":"Artificial intelligence for drug toxicity and safety","volume":"40","author":"Basile","year":"2019","journal-title":"Trends Pharmacol Sci"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib11","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s40264-022-01176-1","article-title":"Artificial intelligence based on machine learning in pharmacovigilance: a scoping review","volume":"45","author":"Kompa","year":"2022","journal-title":"Drug Saf"},{"issue":"2","key":"10.1016\/j.sapharm.2026.09.001_bib12","doi-asserted-by":"crossref","first-page":"266","DOI":"10.3390\/pharmaceutics14020266","article-title":"Using machine learning for pharmacovigilance: a systematic review","volume":"14","author":"Pilipiec","year":"2022","journal-title":"Pharmaceutics"},{"key":"10.1016\/j.sapharm.2026.09.001_bib13","article-title":"Machine learning applications in pharmacovigilance: scoping review","volume":"2","author":"Saleh","year":"2023","journal-title":"Pharmacovigilance"},{"key":"10.1016\/j.sapharm.2026.09.001_bib14","first-page":"1","article-title":"Safeguarding patients in the AI era: ethics at the forefront of pharmacovigilance","author":"Jain","year":"2024","journal-title":"Drug Saf"},{"issue":"6","key":"10.1016\/j.sapharm.2026.09.001_bib15","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.sapharm.2025.02.008","article-title":"Artificial intelligence (AI) in pharmacovigilance: a systematic review on predicting adverse drug reactions (ADR) in hospitalized patients","volume":"21","author":"Dsouza","year":"2025","journal-title":"Res Soc Adm Pharm"},{"key":"10.1016\/j.sapharm.2026.09.001_bib16","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"Br Med J"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1097\/XEB.0000000000000277","article-title":"Updated methodological guidance for the conduct of scoping reviews","volume":"19","author":"Peters","year":"2021","journal-title":"JBI Evid Implement"},{"key":"10.1016\/j.sapharm.2026.09.001_bib18","series-title":"Endnote. Version Endnote X7. Clarivate","year":"2013"},{"issue":"9","key":"10.1016\/j.sapharm.2026.09.001_bib19","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.35377","article-title":"APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support","volume":"6","author":"Kwong","year":"2023","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.sapharm.2026.09.001_bib20","series-title":"Cochrane Handbook for Systematic Reviews of Interventions","article-title":"Chapter 21: qualitative evidence","author":"Noyes","year":"2023"},{"key":"10.1016\/j.sapharm.2026.09.001_bib21","series-title":"The GRADE Handbook","author":"Sch\u00fcnemann","year":"2013"},{"issue":"11","key":"10.1016\/j.sapharm.2026.09.001_bib22","first-page":"4074","article-title":"IL-4\/13 blockade and sleep-related adverse drug reactions in over 37,000 Dupilumab reports from the world health organization individual case safety reporting pharmacovigilance database (VigiBase\u2122): a big data and machine learning analysis","volume":"26","author":"Alroobaea","year":"2022","journal-title":"Eur Rev Med Pharmacol Sci"},{"issue":"2","key":"10.1016\/j.sapharm.2026.09.001_bib23","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.32604\/iasc.2022.022104","article-title":"From similarities to probabilities: feature engineering for predicting drugs' adverse reactions","volume":"32","author":"Barakat","year":"2022","journal-title":"Intell Autom Soft Comput"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s40264-020-00996-3","article-title":"ADE eval: an evaluation of text processing systems for adverse event extraction from drug labels for pharmacovigilance","volume":"44","author":"Bayer","year":"2021","journal-title":"Drug Saf"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib25","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1186\/s12911-021-01518-6","article-title":"Investigating ADR mechanisms with explainable AI: a feasibility study with knowledge graph mining","volume":"21","author":"Bresso","year":"2021","journal-title":"BMC Med Inf Decis Making"},{"issue":"2","key":"10.1016\/j.sapharm.2026.09.001_bib26","doi-asserted-by":"crossref","DOI":"10.3390\/biom13020387","article-title":"Using GPT-3 to build a lexicon of drugs of abuse synonyms for social media pharmacovigilance","volume":"13","author":"Carpenter","year":"2023","journal-title":"Biomolecules"},{"key":"10.1016\/j.sapharm.2026.09.001_bib27","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2024.1401810","article-title":"Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost","volume":"7","author":"Chen","year":"2024","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.sapharm.2026.09.001_bib28","doi-asserted-by":"crossref","DOI":"10.2196\/49643","article-title":"Data-driven identification of factors that influence the quality of adverse event reports: 15-year interpretable machine learning and time-series analyses of VigiBase and QUEST","volume":"12","author":"Choo","year":"2024","journal-title":"JMIR Med Inform"},{"issue":"12","key":"10.1016\/j.sapharm.2026.09.001_bib29","doi-asserted-by":"crossref","DOI":"10.2196\/28632","article-title":"Text mining of adverse events in clinical trials: deep learning approach","volume":"9","author":"Chopard","year":"2021","journal-title":"JMIR Med Inform"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib30","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s12880-024-01349-7","article-title":"Advancing medical imaging: detecting polypharmacy and adverse drug effects with graph convolutional networks (GCN)","volume":"24","author":"Dara","year":"2024","journal-title":"BMC Med Imag"},{"issue":"10","key":"10.1016\/j.sapharm.2026.09.001_bib31","doi-asserted-by":"crossref","DOI":"10.2196\/32730","article-title":"Adverse drug event prediction using noisy literature-derived knowledge graphs: algorithm development and validation","volume":"9","author":"Dasgupta","year":"2021","journal-title":"JMIR Med Inform"},{"issue":"7","key":"10.1016\/j.sapharm.2026.09.001_bib32","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009053","article-title":"Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort","volume":"17","author":"Datta","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"10.1016\/j.sapharm.2026.09.001_bib33","doi-asserted-by":"crossref","DOI":"10.1177\/20420986231219472","article-title":"Evaluation of four machine learning models for signal detection","volume":"14","author":"Dauner","year":"2023","journal-title":"Ther Adv Drug Saf"},{"issue":"6","key":"10.1016\/j.sapharm.2026.09.001_bib34","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1007\/s12325-024-02870-5","article-title":"A pilot, predictive surveillance model in pharmacovigilance using machine learning approaches","volume":"41","author":"De Abreu Ferreira","year":"2024","journal-title":"Adv Ther"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib35","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/ph14050429","article-title":"Cold-start problems in data-driven prediction of drug\u2013drug interaction effects","volume":"14","author":"Dewulf","year":"2021","journal-title":"Pharmaceuticals"},{"issue":"8","key":"10.1016\/j.sapharm.2026.09.001_bib36","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s40264-023-01322-3","article-title":"Provision and characterization of a corpus for pharmaceutical, biomedical named entity recognition for pharmacovigilance: evaluation of language registers and training data sufficiency","volume":"46","author":"Dietrich","year":"2023","journal-title":"Drug Saf"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib37","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1038\/s41598-023-51004-4","article-title":"Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network","volume":"14","author":"Dil\u00e1n-Pantojas","year":"2024","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib38","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-13894-8","article-title":"Automated gathering of real-world data from online patient forums can complement pharmacovigilance for rare cancers","volume":"12","author":"Dirkson","year":"2022","journal-title":"Sci Rep"},{"key":"10.1016\/j.sapharm.2026.09.001_bib39","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2024.1392180","article-title":"BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices","volume":"12","author":"Dong","year":"2024","journal-title":"Front Public Health"},{"issue":"7","key":"10.1016\/j.sapharm.2026.09.001_bib40","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1093\/jamia\/ocab014","article-title":"Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning","volume":"28","author":"Du","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib41","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1093\/jamia\/ocaa218","article-title":"Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text","volume":"28","author":"Fan","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"10.1016\/j.sapharm.2026.09.001_bib42","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1002\/pds.5553","article-title":"Development of a multivariate prediction model to identify individual case safety reports which require clinical review","volume":"31","author":"Gosselt","year":"2022","journal-title":"Pharmacoepidemiol Drug Saf"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib43","article-title":"Predicting adverse drug reactions from social media posts: data balance, feature selection and deep learning","volume":"10","author":"Huang","year":"2022","journal-title":"Healthcare (Basel)"},{"key":"10.1016\/j.sapharm.2026.09.001_bib44","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/5589829","article-title":"Pharmacovigilance with transformers: a framework to detect adverse drug reactions using BERT fine-tuned with FARM","volume":"2021","author":"Hussain","year":"2021","journal-title":"Comput Math Methods Med"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib45","doi-asserted-by":"crossref","DOI":"10.2196\/32543","article-title":"Artificial intelligence-enabled social media analysis for pharmacovigilance of COVID-19 vaccinations in the United Kingdom: observational study","volume":"8","author":"Hussain","year":"2022","journal-title":"JMIR Public Health Surveill"},{"key":"10.1016\/j.sapharm.2026.09.001_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2021.100699","article-title":"Similarity-based machine learning framework for predicting safety signals of adverse drug\u2013drug interactions","volume":"26","author":"Ibrahim","year":"2021","journal-title":"Inform Med Unlocked"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib47","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/s40264-022-01159-2","article-title":"Supervised machine learning-based decision support for signal validation classification","volume":"45","author":"Imran","year":"2022","journal-title":"Drug Saf"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib48","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/TCBB.2019.2928305","article-title":"Improved adverse drug event prediction through information component guided pharmacological network model (IC-PNM)","volume":"18","author":"Ji","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"10.1016\/j.sapharm.2026.09.001_bib49","first-page":"767","article-title":"Extraction of medication-effect relations in Twitter data with neural embedding and recurrent neural network","volume":"290","author":"Jiang","year":"2022","journal-title":"Stud Health Technol Inf"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib50","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-64466-x","article-title":"A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach","volume":"14","author":"Jung","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.sapharm.2026.09.001_bib51","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2022.1011492","article-title":"Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients","volume":"13","author":"Zhu","year":"2022","journal-title":"Front Endocrinol"},{"issue":"8","key":"10.1016\/j.sapharm.2026.09.001_bib52","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s40264-023-01323-2","article-title":"Automatic extraction of comprehensive drug safety information from adverse drug event narratives in the Korea adverse event reporting system using natural language processing techniques","volume":"46","author":"Kim","year":"2023","journal-title":"Drug Saf"},{"issue":"31","key":"10.1016\/j.sapharm.2026.09.001_bib53","doi-asserted-by":"crossref","DOI":"10.3346\/jkms.2021.36.e198","article-title":"Machine learning approach for active vaccine safety monitoring","volume":"36","author":"Kim","year":"2021","journal-title":"J Kor Med Sci"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib54","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1093\/jamia\/ocad197","article-title":"Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA adverse event reporting System (FAERS)","volume":"31","author":"Konkel","year":"2023","journal-title":"J Am Med Inform Assoc"},{"issue":"9","key":"10.1016\/j.sapharm.2026.09.001_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e19441","article-title":"MultiGML: multimodal graph machine learning for prediction of adverse drug events","volume":"9","author":"Krix","year":"2023","journal-title":"Heliyon"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib56","article-title":"Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system","volume":"12","author":"Lee","year":"2022","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.sapharm.2026.09.001_bib57","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1002\/cpt.2266","article-title":"Artificial intelligence for unstructured healthcare data: application to coding of patient reporting of adverse drug reactions","volume":"110","author":"L\u00e9tinier","year":"2021","journal-title":"Clin Pharmacol Ther"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib58","article-title":"Using a machine learning approach to monitor COVID-19 Vaccine adverse events (VAE) from Twitter data","volume":"10","author":"Lian","year":"2022","journal-title":"Vaccines (Basel)"},{"issue":"10","key":"10.1016\/j.sapharm.2026.09.001_bib59","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.1093\/jamia\/ocab114","article-title":"DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter","volume":"28","author":"Magge","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"10.1016\/j.sapharm.2026.09.001_bib60","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2021.103719","article-title":"Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance","volume":"117","author":"Malec","year":"2021","journal-title":"J Biomed Inf"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib61","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1186\/s12911-022-02085-0","article-title":"LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning","volume":"22","author":"Martenot","year":"2022","journal-title":"BMC Med Inf Decis Making"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib62","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s40264-022-01153-8","article-title":"Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data","volume":"45","author":"Martin","year":"2022","journal-title":"Drug Saf"},{"key":"10.1016\/j.sapharm.2026.09.001_bib63","doi-asserted-by":"crossref","DOI":"10.3389\/fddsv.2021.768792","article-title":"Deep learning prediction of adverse drug reactions in drug discovery using open TG\u2013GATEs and FAERS databases","volume":"1","author":"Mohsen","year":"2021","journal-title":"Frontiers in Drug Discovery"},{"key":"10.1016\/j.sapharm.2026.09.001_bib64","first-page":"878","article-title":"Named entity recognition in pubmed abstracts for pharmacovigilance using deep learning","volume":"294","author":"Nghiem","year":"2022","journal-title":"Stud Health Technol Inf"},{"key":"10.1016\/j.sapharm.2026.09.001_bib65","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2023.106314","article-title":"Toward AI-supported evaluation for safety control measures against near-miss events in pharmaceutical products","volume":"168","author":"Okamoto","year":"2023","journal-title":"Saf Sci"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib66","doi-asserted-by":"crossref","DOI":"10.1177\/14604582221136712","article-title":"Identifying adverse drug reactions from patient reviews on social media using natural language processing","volume":"29","author":"Oyebode","year":"2023","journal-title":"Health Informatics J"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib67","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s13181-022-00906-2","article-title":"Artificial intelligent context-aware machine-learning tool to detect adverse drug events from social media platforms","volume":"18","author":"Roosan","year":"2022","journal-title":"J Med Toxicol"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib68","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1002\/cpt.2074","article-title":"Target adverse event profiles for predictive safety in the postmarket setting","volume":"109","author":"Schotland","year":"2021","journal-title":"Clin Pharmacol Ther"},{"key":"10.1016\/j.sapharm.2026.09.001_bib69","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2023.1271309","article-title":"Big data- and machine learning-based analysis of a global pharmacovigilance database enables the discovery of sex-specific differences in the safety profile of dual IL4\/IL13 blockade","volume":"14","author":"Sharif","year":"2023","journal-title":"Front Pharmacol"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib70","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011989","article-title":"SubGE-DDI: a new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement","volume":"20","author":"Shi","year":"2024","journal-title":"PLoS Comput Biol"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib71","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1177\/1074248421995348","article-title":"Prediction of drug-induced long QT syndrome using machine learning applied to harmonized electronic health record data","volume":"26","author":"Simon","year":"2021","journal-title":"J Cardiovasc Pharmacol Therapeut"},{"key":"10.1016\/j.sapharm.2026.09.001_bib72","doi-asserted-by":"crossref","DOI":"10.2196\/45000","article-title":"Artificial intelligence-enabled software prototype to inform opioid pharmacovigilance from electronic health records: development and usability study","volume":"2","author":"Sorbello","year":"2023","journal-title":"Jmir ai"},{"key":"10.1016\/j.sapharm.2026.09.001_bib73","doi-asserted-by":"crossref","first-page":"90828","DOI":"10.1109\/ACCESS.2021.3091394","article-title":"Sentence-level aspect-based sentiment analysis for classifying adverse drug reactions (ADRs) using hybrid Ontology-XLNet transfer learning","volume":"9","author":"Sweidan","year":"2021","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.sapharm.2026.09.001_bib74","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1093\/bioinformatics\/btaa675","article-title":"The Russian drug reaction corpus and neural models for drug reactions and effectiveness detection in user reviews","volume":"37","author":"Tutubalina","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.sapharm.2026.09.001_bib75","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2020.625659","article-title":"Adverse drug reaction discovery using a tumor-biomarker knowledge graph","volume":"11","author":"Wang","year":"2021","journal-title":"Front Genet"},{"key":"10.1016\/j.sapharm.2026.09.001_bib76","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2021.659622","article-title":"InferBERT: a transformer-based causal inference framework for enhancing pharmacovigilance","volume":"4","author":"Wang","year":"2021","journal-title":"Front Artif Intell"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib77","doi-asserted-by":"crossref","DOI":"10.1093\/jamiaopen\/ooab081","article-title":"Deep learning models in detection of dietary supplement adverse event signals from Twitter","volume":"4","author":"Wang","year":"2021","journal-title":"JAMIA Open"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib78","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1248\/bpb.b23-00846","article-title":"Streamlining considerations for safety measures: a predictive model for addition of clinically significant adverse reactions to Japanese drug package inserts","volume":"47","author":"Watanabe","year":"2024","journal-title":"Biol Pharm Bull"},{"issue":"2","key":"10.1016\/j.sapharm.2026.09.001_bib79","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s43441-023-00603-4","article-title":"Predicting the addition of information regarding clinically significant adverse drug reactions to Japanese drug package inserts using a machine-learning model","volume":"58","author":"Watanabe","year":"2024","journal-title":"Ther Innov Regul Sci"},{"key":"10.1016\/j.sapharm.2026.09.001_bib80","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/6033860","article-title":"Evaluating drug risk using GAN and SMOTE based on CFDA's spontaneous reporting data","volume":"2021","author":"Wei","year":"2021","journal-title":"J Healthc Eng"},{"issue":"12","key":"10.1016\/j.sapharm.2026.09.001_bib81","doi-asserted-by":"crossref","DOI":"10.2196\/26407","article-title":"Chinese-named entity recognition from adverse drug event records: radical embedding-combined dynamic embedding-based BERT in a bidirectional long short-term conditional random field (Bi-LSTM-CRF) model","volume":"9","author":"Wu","year":"2021","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.sapharm.2026.09.001_bib82","first-page":"10doi","article-title":"Data mining methodology for response to hypertension symptomology-application to COVID-19-related pharmacovigilance","author":"Xu","year":"2021","journal-title":"eLife"},{"issue":"9","key":"10.1016\/j.sapharm.2026.09.001_bib83","doi-asserted-by":"crossref","DOI":"10.2196\/38140","article-title":"An assessment of mentions of adverse drug events on social media with natural language processing: model development and analysis","volume":"10","author":"Yu","year":"2022","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.sapharm.2026.09.001_bib84","article-title":"Predicting adverse drug events in Chinese pediatric inpatients with the associated risk factors: a machine learning study","volume":"12","author":"Yu","year":"2021","journal-title":"Front Pharmacol"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib85","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1038\/s42003-023-05243-w","article-title":"Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework","volume":"6","author":"Zhao","year":"2023","journal-title":"Commun Biol"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib86","doi-asserted-by":"crossref","DOI":"10.2196\/30426","article-title":"Identifying cases of shoulder injury related to vaccine administration (SIRVA) in the United States: development and validation of a natural language processing method","volume":"8","author":"Zheng","year":"2022","journal-title":"JMIR Public Health Surveill"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib87","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1111\/bcpt.13773","article-title":"Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records","volume":"131","author":"Kaas-Hansen","year":"2022","journal-title":"Basic Clin Pharmacol Toxicol"},{"issue":"10","key":"10.1016\/j.sapharm.2026.09.001_bib88","doi-asserted-by":"crossref","DOI":"10.2196\/27714","article-title":"Quantifying the severity of adverse drug reactions using social media: network analysis","volume":"23","author":"Lavertu","year":"2021","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.sapharm.2026.09.001_bib89","series-title":"Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.sapharm.2026.09.001_bib90","series-title":"Proceedings of NAACL-HLT. Minneapolis, Minnesota","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib91","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1002\/cpt.302","article-title":"Big data and adverse drug reaction detection","volume":"99","author":"Harpaz","year":"2016","journal-title":"Clin Pharmacol Therapeut"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib92","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1021\/acs.jcim.3c01304","article-title":"Comprehensive review of drug\u2013drug interaction prediction based on machine learning: current status, challenges, and opportunities","volume":"64","author":"Wang","year":"2024","journal-title":"J Chem Inf Model"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib93","doi-asserted-by":"crossref","DOI":"10.1093\/jamiaopen\/ooad079","article-title":"How can artificial intelligence decrease cognitive and work burden for front line practitioners?","volume":"6","author":"Gandhi","year":"2023","journal-title":"JAMIA Open"},{"key":"10.1016\/j.sapharm.2026.09.001_bib94","series-title":"And Clinical Integration","first-page":"2308","article-title":"Clinicians' perceptions of artificial intelligence: focus on workload, risk, trust, clinical decision making","author":"Shamszare","year":"2023"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib95","article-title":"Effect of uncertainty-aware AI models on pharmacists' reaction time and decision-making in a web-based mock medication verification task: randomized controlled trial","volume":"13","author":"Lester","year":"2025","journal-title":"JMIR Med Inform"},{"issue":"4","key":"10.1016\/j.sapharm.2026.09.001_bib96","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1007\/s43441-019-00023-3","article-title":"Utilizing advanced technologies to augment pharmacovigilance systems: challenges and opportunities","volume":"54","author":"Lewis","year":"2020","journal-title":"Ther Innov Regulat Sci"},{"issue":"5","key":"10.1016\/j.sapharm.2026.09.001_bib97","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s40264-022-01164-5","article-title":"Industry perspective on artificial intelligence\/machine learning in pharmacovigilance: perspective on AI and ML in PV","volume":"45","author":"Kassekert","year":"2022","journal-title":"Drug Saf"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib98","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s40290-019-00320-0","article-title":"Automation Opportunities in Pharmacovigilance: an Industry Survey: R. Ghosh et al","volume":"34","author":"Ghosh","year":"2020","journal-title":"Pharmac Med"},{"key":"10.1016\/j.sapharm.2026.09.001_bib99","series-title":"Big Data Steering Group (BDSG): 2024 Report","year":"2024"},{"key":"10.1016\/j.sapharm.2026.09.001_bib100","series-title":"Artificial Intelligence in Pharmacovigilance: CIOMS Working Group Report","first-page":"204","year":"2025"},{"key":"10.1016\/j.sapharm.2026.09.001_bib101","series-title":"Artificial Intelligence in Healthcare","first-page":"295","article-title":"Ethical and legal challenges of artificial intelligence-driven healthcare","author":"Gerke","year":"2020"},{"issue":"6","key":"10.1016\/j.sapharm.2026.09.001_bib103","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1111\/cts.13267","article-title":"Advancing an agile regulatory ecosystem to respond to the rapid development of innovative technologies","volume":"15","author":"ElZarrad","year":"2022","journal-title":"Clin Transl Sci"},{"key":"10.1016\/j.sapharm.2026.09.001_bib104","series-title":"Discussion Paper","article-title":"Using artificial intelligence & machine learning in the development of drug & biological product","year":"2025"},{"issue":"1","key":"10.1016\/j.sapharm.2026.09.001_bib105","article-title":"General data protection regulation","volume":"24","author":"Protection","year":"2018","journal-title":"Intersoft Consult"},{"issue":"6","key":"10.1016\/j.sapharm.2026.09.001_bib106","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"35","key":"10.1016\/j.sapharm.2026.09.001_bib107","first-page":"9908","article-title":"International conference on harmonisation; E2B (R3) electronic transmission of individual case safety reports; data elements and message specification; appendix on backwards and forwards compatibility; availability. Notice","volume":"79","year":"2014","journal-title":"Fed Regist"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib108","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s40264-020-01030-2","article-title":"Validating Intelligent Automation Systems in Pharmacovigilance: insights from Good Manufacturing Practices: K. Huysentruyt et al","volume":"44","author":"Huysentruyt","year":"2021","journal-title":"Drug Saf"},{"issue":"3","key":"10.1016\/j.sapharm.2026.09.001_bib109","doi-asserted-by":"crossref","first-page":"116","DOI":"10.4103\/picr.picr_290_23","article-title":"Artificial intelligence in pharmacovigilance\u2013opportunities and challenges","volume":"15","author":"Desai","year":"2024","journal-title":"Perspect Clin Res"},{"issue":"6","key":"10.1016\/j.sapharm.2026.09.001_bib110","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","article-title":"Algorithmic fairness in artificial intelligence for medicine and healthcare","volume":"7","author":"Chen","year":"2023","journal-title":"Nat Biomed Eng"},{"issue":"8","key":"10.1016\/j.sapharm.2026.09.001_bib111","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1038\/s42256-023-00697-3","article-title":"Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning","volume":"5","author":"Yang","year":"2023","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.sapharm.2026.09.001_bib112","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1016\/S0140-6736(17)31047-4","article-title":"Medication without harm: who's third global patient safety challenge","volume":"389","author":"Donaldson","year":"2017","journal-title":"Lancet"},{"issue":"6464","key":"10.1016\/j.sapharm.2026.09.001_bib113","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.sapharm.2026.09.001_bib114","series-title":"Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle","year":"2023"}],"container-title":["Research in Social and Administrative Pharmacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1551741126001919?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1551741126001919?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T05:19:10Z","timestamp":1794287950000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1551741126001919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":112,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,1]]}},"alternative-id":["S1551741126001919"],"URL":"https:\/\/doi.org\/10.1016\/j.sapharm.2026.09.001","relation":{},"ISSN":["1551-7411"],"issn-type":[{"value":"1551-7411","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integration of artificial intelligence into pharmacovigilance: A systematic review","name":"articletitle","label":"Article Title"},{"value":"Research in Social and Administrative Pharmacy","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.sapharm.2026.09.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}