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Data Science Impacting the Pharmaceutical Industry Part II: Clinical Trial Applications

  • ID: 5146588
  • Report
  • August 2020
  • Region: Global
  • 67 Pages
  • Frost & Sullivan

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FEATURED COMPANIES

  • Ai Cure
  • Antidote
  • BullFrog Ai
  • Deep 6 Ai
  • GNS Healthcare
  • Inato
  • MORE

Clinical Trials, the most crucial step in drug discovery has never been in the forefront in adopting new technologies and transforming from the conventional protocols. With the development of AI/ML in the last five years, it has opened up a new horizon for transforming clinical trials. It has been proven that use of AI/ML tools can scan through large volumes of data and provide accurate results within minutes which takes enormous time when performed by humans. Application of analytical tools help identify trends and patterns that may have been difficult to observe otherwise and provide results in the best possible way.

Note: Product cover images may vary from those shown

FEATURED COMPANIES

  • Ai Cure
  • Antidote
  • BullFrog Ai
  • Deep 6 Ai
  • GNS Healthcare
  • Inato
  • MORE

Chapter 1 - Overview of Clinical Trials and Data Science
1.1 Clinical Trials Still the most Complex-Step in the Lifecycle of Drug Development
1.2 Following Conventional Steps in Clinical Trials is an Ongoing Challenge
1.3 Importance of Data Science in Clinical Trials
1.4 Important Terminologies Associated with Data Science and its Role

Chapter 2 - Adoption of Data Science in Clinical Trials
2.1 Adoption of Data Science in Clinical Trials is Seen across the Globe
2.2 Key Factors to Leverage Use of Data Science in Clinical Trials
2.3 NLP and OCR are Extensively Used in Data Structuring
2.4 Data Science Taps Untapped Significant Information
2.5 Data science Tools Help Reduce Clinical Trial Timeline
2.6 Downstream Workflow of Data Science Tools
2.7 Medical Data Conversion to E-health Data is an Essential Step

Chapter 3 - Application of Data Science in Clinical Trials
3.1 AI Based Models Provide Predictive Solutions
3.2 Data Driven Models’ Focus Areas
3.3 Key Challenges in Patient Enrollment
3.4 Use of Digital Platforms Increases Patient Retention
3.5 Role of Data Science in Leveraging Cancer Treatments
3.6 Role of Data Science in the Rare Disease Area
3.7 Enabling Virtual Clinical Trials Using Data Science during COVID-19
3.8 AI Tools Speed up Patient Recruitment
3.9 Data Sharing to be Driving Factor in AI Enabled Clinical Trials

Chapter 4 - Companies to Action
4.1 Collaboration between Big Pharma and AI Companies to Leverage Clinical Trials
4.2 Tech Companies Come together to Pool Resources
4.3 Mendel.ai
4.4 AI Cure
4.5 Inato
4.6 GNS Healthcare
4.7 Trials.ai
4.8 Antidote
4.9 Deep 6 AI
4.10 BullFrog AI
4.11 PathAI
4.12 Tempus

Chapter 5 - IP Analysis
5.1 IP Overview of Data Science in Pharmaceutical Development
5.2 Top Patent List
5.3 Top Patents in Screening Patients Technology
5.4 Top Patents in Structured Platforms

Chapter 6 - Growth Opportunities
6.1 Growth opportunity 1: Data Science-driven Patient Selection
6.2 Growth opportunity 2: FDA accelerated Data sharing has opened many opportunities

7. Appendix
7.1 Abbreviations
7.2 Key Industry Contacts

Strategic Imperatives
i) The Strategic Imperative 8™
ii) The Impact of the Top Three Strategic Imperatives on the Data Science Industry
iii) About The Growth Pipeline Engine™
iv) Growth Opportunities Fuel the Growth Pipeline Engine™
v) Research Methodology
vi) Key Findings

Note: Product cover images may vary from those shown
  • Ai Cure
  • Antidote
  • BullFrog Ai
  • Deep 6 Ai
  • GNS Healthcare
  • Inato
  • Mendel.ai
  • PathAI
  • Tempus
  • Trials.ai
Note: Product cover images may vary from those shown
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