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Chapter 1 Introduction to smart pharma: foundation of AI and big data

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

Due to its potential to completely transform the pharmaceutical business, artificial intelligence (AI) has emerged as a potent instrument in the medical community. However, the potential isn’t fully realized yet. This chapter provides information about AI and big data in the pharma and other healthcare industries. Nevertheless, AI techniques such as big data and natural language processing (NLP) can expedite and rehabilitate the process by facilitating a more precise and efficient analysis of large datasets. To achieve this, the test administration may be simplified. The application of AI in the field of chemistry has recently garnered significant attention due to its potential to provide valuable insights into developing efficient drug delivery systems. AI significantly influences the medication manufacturing process and the drug repurposing strategy. Despite its numerous obstacles, AI has the potential to enhance medical imaging services and help diagnose patients. However, healthcare systems are responsible for informing patients about the benefits and drawbacks of AI-supported healthcare systems. A conclusion of the recent state of the field for AI applications in the healthcare industry is provided in the chapter, taking into account the rapid advancements in AI combined with AI-integrated medical systems.

Abstract

Due to its potential to completely transform the pharmaceutical business, artificial intelligence (AI) has emerged as a potent instrument in the medical community. However, the potential isn’t fully realized yet. This chapter provides information about AI and big data in the pharma and other healthcare industries. Nevertheless, AI techniques such as big data and natural language processing (NLP) can expedite and rehabilitate the process by facilitating a more precise and efficient analysis of large datasets. To achieve this, the test administration may be simplified. The application of AI in the field of chemistry has recently garnered significant attention due to its potential to provide valuable insights into developing efficient drug delivery systems. AI significantly influences the medication manufacturing process and the drug repurposing strategy. Despite its numerous obstacles, AI has the potential to enhance medical imaging services and help diagnose patients. However, healthcare systems are responsible for informing patients about the benefits and drawbacks of AI-supported healthcare systems. A conclusion of the recent state of the field for AI applications in the healthcare industry is provided in the chapter, taking into account the rapid advancements in AI combined with AI-integrated medical systems.

Kapitel in diesem Buch

  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/10.1515/9783111574288-001

Kapitel in diesem Buch

  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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Heruntergeladen am 23.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/9783111574288-001/html?lang=de
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