PHARMACOVIGILANCE IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Miss Patil Jyoti Narayan; Mrs Asmi M Kadam; Miss Snehal Trimbak Daud; Ms Minakshi Ajamal Jadhav; Mr  Kuldeep Sukhdeo Kanadje

PHARMACOVIGILANCE IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Authors: Miss Patil Jyoti Narayan, Mrs Asmi M Kadam, Miss Snehal Trimbak Daud, Ms Minakshi Ajamal Jadhav, Mr Kuldeep Sukhdeo Kanadje

Unique Paper ID: 168731
Volume: 11
Issue: 5
PageNo: 1839-1847

Keywords: Pharmacovigilance Artificial Intelligence (AI) Machine Learning (ML) Adverse Drug Reactions (ADRs) Signal Detection Data Mining Natural Language Processing (NLP) Predictive Analytics Real-Time Monitoring

Abstract:
Summarize the key points of the review, including the role of AI and ML in improving drug safety, challenges in traditional pharmacovigilance, current applications, and future directions. Pharmacovigilance (PV), the science of detecting, assessing, understanding, and preventing adverse drug reactions (ADRs), plays a critical role in ensuring patient safety and drug efficacy. Traditionally, pharmacovigilance has relied heavily on manual reporting systems, spontaneous reports, and clinical trial data, all of which have limitations such as underreporting, delays in signal detection, and the inability to manage large datasets efficiently. However, the advent of artificial intelligence (AI) and machine learning (ML) is revolutionizing the pharmacovigilance landscape by providing advanced tools for automating data collection, processing vast amounts of realworld data (RWD), and predicting ADRs with greater speed and accuracy. This review explores the transformative impact of AI and ML technologies on pharmacovigilance processes. AIpowered tools such as natural language processing (NLP), text mining, and predictive modeling allow for the automation of adverse event reporting, rapid signal detection, and integration of diverse data sources, including electronic health records (EHRs), social media, and patientreported outcomes. The use of machine learning algorithms, such as decision trees, random forests, and deep learning networks, has enabled Realtime monitoring and detection of safety signals, significantly improving the efficiency of post marketing surveillance.

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 5
PageNo: 1839-1847

PHARMACOVIGILANCE IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Available:https://ijirt.org/Article?manuscript=168731

27 Nov, 2024

NEXT-GENERATION HUMAN CELL CULTURE MODELS: REVOLUTIONIZING DRUG DISCOVERY AND SAFETY ASSESSMENT

20 Nov, 2024

ANTI-AGING FORMULATION USING HERBAL CREAMS

04 Oct, 2024

"Dissecting Ischemic Stroke: Mechanistic Insights from In Vivo and In Vitro Models"

08 Sep, 2024

The Role of Artificial Intelligence in Improving Diagnostic Precision and Patient Care

26 Aug, 2024

MONKEYPOX: THE NEXT GLOBAL HEALTH CRISIS

21 Aug, 2024

ADVANCES IN REPRODUCTIVE TOXICOLOGY: SCREENING APPROACHES IN FEMALE MODELS

07 Aug, 2024

Catharanthus Roseus: A powerful Ally In Cancer Treatment

11 Jul, 2024

CANCER VACCINES: PIONEERING APPROACHES IN IMMUNOTHERAPY

23 Jun, 2024

Nature's Resilience Boosters: Exploring Adaptogenic Herbs for Disease Management

13 Jun, 2024

Development and Assessment of Lip Balm Incorporating a Variety of Herbal Extracts

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry