Home Technology Chapter 7 AI-driven clinical decision support systems for pharma executives
Chapter
Licensed
Unlicensed Requires Authentication

Chapter 7 AI-driven clinical decision support systems for pharma executives

  • Rishabha Malviya , Shristy Verma , Sonali Sundram and Harshil Shah
Become an author with De Gruyter Brill
Explore this Subject How to publish with us
Digital Blockchain
This chapter is in the book Digital Blockchain

Abstract

Artificial intelligence (AI) is a powerful tool that makes data processing, analysis, and interpretation possible. The following chapter offers information on the AI algorithms employed by pharmaceutical corporations in their clinical decision support systems. There are difficulties in implementing AI-CDSS, and future possibilities are also covered. AI-based algorithms such as logistic regression, support vector computations, naive Bayes, random forests, decision tree models, and ANN are expected to be critical in optimizing medication compositions and enhancing treatment outcomes, while also anticipating potential toxicity along with side effects in patientspecific dose formulation and safety evaluations. AI-powered CDSS helps doctors, nurses, and patients make informed decisions by evaluating patient data. NLP improves patient communication and electronic health records. Through meticulous analysis of the patient’s signs, symptoms, medical records, and contextual information, an NLP-driven Clinical Decision Support System can propose potential diagnoses, treatment strategies, and pharmacological interventions. These difficulties include organizational and social factors, human factors, and a lack of confidence among medical professionals.

Abstract

Artificial intelligence (AI) is a powerful tool that makes data processing, analysis, and interpretation possible. The following chapter offers information on the AI algorithms employed by pharmaceutical corporations in their clinical decision support systems. There are difficulties in implementing AI-CDSS, and future possibilities are also covered. AI-based algorithms such as logistic regression, support vector computations, naive Bayes, random forests, decision tree models, and ANN are expected to be critical in optimizing medication compositions and enhancing treatment outcomes, while also anticipating potential toxicity along with side effects in patientspecific dose formulation and safety evaluations. AI-powered CDSS helps doctors, nurses, and patients make informed decisions by evaluating patient data. NLP improves patient communication and electronic health records. Through meticulous analysis of the patient’s signs, symptoms, medical records, and contextual information, an NLP-driven Clinical Decision Support System can propose potential diagnoses, treatment strategies, and pharmacological interventions. These difficulties include organizational and social factors, human factors, and a lack of confidence among medical professionals.

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/10.1515/9783111574288-007

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
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 23.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/9783111574288-007/html?lang=en
Scroll to top button