{"title":"药物警戒中的人工智能:推进药物安全监测和监管整合。","authors":"Ankit Nagar, Joga Gobburu, Aloka Chakravarty","doi":"10.1177/20420986251361435","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) has rapidly evolved from experimental applications in pharmacovigilance (PV) to being considered for routine use. This review critically examines AI's potential to revolutionize drug safety monitoring, focusing on practical implementation challenges such as ensuring AI's consistent and transparent performance, reducing multiple sources of bias, and addressing interpretability issues. It emphasizes the transition from experimental use to a routine, scalable capability within PV. It examines AI's evidence base in specific applications, its ability to enhance actionable insights, and how organizations can safeguard against unintended consequences in multi-AI system environments. These considerations are vital as AI moves from theory to practice in PV.</p>","PeriodicalId":23012,"journal":{"name":"Therapeutic Advances in Drug Safety","volume":"16 ","pages":"20420986251361435"},"PeriodicalIF":3.4000,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317250/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration.\",\"authors\":\"Ankit Nagar, Joga Gobburu, Aloka Chakravarty\",\"doi\":\"10.1177/20420986251361435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Artificial intelligence (AI) has rapidly evolved from experimental applications in pharmacovigilance (PV) to being considered for routine use. This review critically examines AI's potential to revolutionize drug safety monitoring, focusing on practical implementation challenges such as ensuring AI's consistent and transparent performance, reducing multiple sources of bias, and addressing interpretability issues. It emphasizes the transition from experimental use to a routine, scalable capability within PV. It examines AI's evidence base in specific applications, its ability to enhance actionable insights, and how organizations can safeguard against unintended consequences in multi-AI system environments. These considerations are vital as AI moves from theory to practice in PV.</p>\",\"PeriodicalId\":23012,\"journal\":{\"name\":\"Therapeutic Advances in Drug Safety\",\"volume\":\"16 \",\"pages\":\"20420986251361435\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317250/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Therapeutic Advances in Drug Safety\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/20420986251361435\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Therapeutic Advances in Drug Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/20420986251361435","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration.
Artificial intelligence (AI) has rapidly evolved from experimental applications in pharmacovigilance (PV) to being considered for routine use. This review critically examines AI's potential to revolutionize drug safety monitoring, focusing on practical implementation challenges such as ensuring AI's consistent and transparent performance, reducing multiple sources of bias, and addressing interpretability issues. It emphasizes the transition from experimental use to a routine, scalable capability within PV. It examines AI's evidence base in specific applications, its ability to enhance actionable insights, and how organizations can safeguard against unintended consequences in multi-AI system environments. These considerations are vital as AI moves from theory to practice in PV.
期刊介绍:
Therapeutic Advances in Drug Safety delivers the highest quality peer-reviewed articles, reviews, and scholarly comment on pioneering efforts and innovative studies pertaining to the safe use of drugs in patients.
The journal has a strong clinical and pharmacological focus and is aimed at clinicians and researchers in drug safety, providing a forum in print and online for publishing the highest quality articles in this area. The editors welcome articles of current interest on research across all areas of drug safety, including therapeutic drug monitoring, pharmacoepidemiology, adverse drug reactions, drug interactions, pharmacokinetics, pharmacovigilance, medication/prescribing errors, risk management, ethics and regulation.