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Chapter 8 AI-based pharmacovigilance and drug safety

  • Rishabha Malviya , Shristy Verma , Sonali Sundram and Harshil Shah
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Abstract

Pharmacovigilance (PV), a scientific and applied field, aims to identify, evaluate, understand, and minimize adverse drug-related effects. The chapter provides information about artificial intelligence-based tools for pharmacovigilance, drug safety, and its regulatory aspects. It encompasses the scientific and applied endeavors made to identify, assess, comprehend, and mitigate the adverse consequences of drugs or possible drug-related issues. ADR detection and reporting, technological AE coding, safety individual reporting, severity assessment, and connection to suspected drugs are among the most significant duties in the PV sector. To gather data regarding adverse drug reactions (ADRs) along with adverse drug events (ADEs), process reports according to international clinical standards, obtain drug-drug interactions, estimate drug side effects, indicate clinical trials, and incorporate estimation unpredictability toward diagnostic classification algorithms, artificial intelligence (AI) is being utilized for pharmacovigilance and patient safety. The lack of cross-specialty opportunities for training, privacy issues, data models for health care organizations, and insufficient education and training in nations with middle and low incomes are some of the challenges that have been addressed in the context of pharmacology as well as drug safety.

Abstract

Pharmacovigilance (PV), a scientific and applied field, aims to identify, evaluate, understand, and minimize adverse drug-related effects. The chapter provides information about artificial intelligence-based tools for pharmacovigilance, drug safety, and its regulatory aspects. It encompasses the scientific and applied endeavors made to identify, assess, comprehend, and mitigate the adverse consequences of drugs or possible drug-related issues. ADR detection and reporting, technological AE coding, safety individual reporting, severity assessment, and connection to suspected drugs are among the most significant duties in the PV sector. To gather data regarding adverse drug reactions (ADRs) along with adverse drug events (ADEs), process reports according to international clinical standards, obtain drug-drug interactions, estimate drug side effects, indicate clinical trials, and incorporate estimation unpredictability toward diagnostic classification algorithms, artificial intelligence (AI) is being utilized for pharmacovigilance and patient safety. The lack of cross-specialty opportunities for training, privacy issues, data models for health care organizations, and insufficient education and training in nations with middle and low incomes are some of the challenges that have been addressed in the context of pharmacology as well as drug safety.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Introduction to smart pharma: foundation of AI and big data 1
  6. Chapter 2 Emerging technology in pharma and the role of AI and blockchain 19
  7. Chapter 3 Partnership and collaboration in the pharmaceutical industry 37
  8. Chapter 4 Education and training in smart pharma 55
  9. Chapter 5 Drug manufacturing and quality control with artificial intelligence 77
  10. Chapter 6 Drug development and clinical trial via artificial intelligence 97
  11. Chapter 7 AI-driven clinical decision support systems for pharma executives 121
  12. Chapter 8 AI-based pharmacovigilance and drug safety 143
  13. Chapter 9 Intellectual property and data privacy in the pharmaceutical sector 163
  14. Chapter 10 Regulatory affairs and compliance in the pharmaceutical sector 183
  15. Chapter 11 AI and big data in post-marketing surveillance 201
  16. Chapter 12 Challenges and ethical considerations 219
  17. Chapter 13 Future trends and innovations of AI in healthcare 237
  18. Index 253
https://doi.org/10.1515/9783111574288-008

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Introduction to smart pharma: foundation of AI and big data 1
  6. Chapter 2 Emerging technology in pharma and the role of AI and blockchain 19
  7. Chapter 3 Partnership and collaboration in the pharmaceutical industry 37
  8. Chapter 4 Education and training in smart pharma 55
  9. Chapter 5 Drug manufacturing and quality control with artificial intelligence 77
  10. Chapter 6 Drug development and clinical trial via artificial intelligence 97
  11. Chapter 7 AI-driven clinical decision support systems for pharma executives 121
  12. Chapter 8 AI-based pharmacovigilance and drug safety 143
  13. Chapter 9 Intellectual property and data privacy in the pharmaceutical sector 163
  14. Chapter 10 Regulatory affairs and compliance in the pharmaceutical sector 183
  15. Chapter 11 AI and big data in post-marketing surveillance 201
  16. Chapter 12 Challenges and ethical considerations 219
  17. Chapter 13 Future trends and innovations of AI in healthcare 237
  18. Index 253
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