1<!DOCTYPE html>
2
3Anonymous
4/bestp
5/bestp/domrep.nsf
6EF1C4F70B064B946002589210033BCCE
8
9
10
11
12
13
140
15
16
17/bestp/domrep.nsf/products/benchmarking-real-world-uses-of-artificial-intelligence-in-medical-affairs
18
19
2034.239.152.207
21
22
23best-in-class.com
24/bestp/domrep.nsf
25BMR




Products & Services Medical Affairs Medical Affairs Excellence

Benchmarking Real-World Uses of Artificial Intelligence in Medical Affairs

ID: POP-356


Features:

10 Info Graphics

25 Data Graphics

370+ Metrics

32 Narratives


Pages: 45


Published: 2023


Delivery Format: Shipped


 

License Options:


Buy Now

 

919-403-0251

  • STUDY OVERVIEW
  • BENCHMARK CLASS
  • STUDY SNAPSHOT
  • KEY FINDINGS
  • VIEW TOC AND LIST OF EXHIBITS
The advancement of artificial intelligence (AI) offers unprecedented opportunities for Medical Affairs to enhance personalized engagement, deepen insights, and drive impact across the biopharma organization. To remain competitive, Medical Affairs leaders must identify where they can integrate AI and machine learning approaches to enhance the strategic value of the function.

Best Practices, LLC undertook this research to benchmark real-world uses of artificial intelligence in Medical Affairs. The report examines how top companies are applying AI within Medical Affairs to enhance personalized engagement of stakeholders, improve data analysis and evidence generation, facilitate omnichannel implementation, modify medical strategy and operations, gather insights, and take critical actions. The report further highlights the top lessons learned for developing and deploying artificial intelligence within a Medical Affairs organization.

Video Brief:

Industries Profiled:
Biopharmaceutical; Medical Device; Pharmaceutical; Manufacturing; Biotech; Consumer Products; Diagnostic; Health Care; Consulting; Communications; Clinical Research; Laboratories


Companies Profiled:
Apellis Pharmaceuticals; ASC Therapeutics; Baxter International; Bayer; Boehringer Ingelheim; Cormedix Inc.; Curis; Daiichi Sankyo; Eisai; EMD Serono; Indivior; Ipsen; Luzsana Biotechnology; Medexus Pharmaceuticals; MEDiSTRAVA; MedThink SciCom; Merck; Novartis; Sanofi; Straumann; Takeda Pharmaceuticals; Teva Pharmaceutical Industries Ltd

Study Snapshot

Best Practices, LLC engaged 29 Medical leaders from 22 pharma companies in this research through a benchmarking survey instrument.

Key topics covered in this report include:

  • Current Use of Artificial Intelligence in Medical Affairs
  • Leveraging AI for Medical Omnichannel Engagement
  • Use of Artificial Intelligence for Personalized Engagement
  • Use of Artificial Intelligence to Improve Data Analysis and Evidence Generation
  • Use of Artificial Intelligence to Finetune Medical Strategy and Operations
  • Artificial Intelligence Success Stories and Key Lessons Learned

Key Findings

Select key insights uncovered from this report are noted below. Detailed findings are available in the full report.

  • Current Use of AI in Medical Affairs: Nearly 60% of surveyed biopharma manufacturers have implemented or are in the process of implementing AI capabilities to capture new insights and improve performance across Medical Affairs.
  • Medical Strategy: Up to 45% of organizations use AI for competitive intelligence insights, literature review, identification of new patient groups, and clinical trial insights (selection/design/recruitment).
Table of Contents

Sr. No.
Topic
Slide No.
I.
Executive SummaryPg. 3-11
II.
Artificial Intelligence in Medical AffairsPg. 12-15
III.
Personalized EngagementPg. 16-22
IV.
Data Analysis & Evidence GenerationPg. 23-29
V.
Strategy & OperationsPg. 30-36
VI.
Insights from AI Pilot ProgramsPg. 37-41
VII.
Participant DemographicsPg. 42-44
VIII.
About Best Practices, LLCPg. 45

    List of Charts & Exhibits

    I. Artificial Intelligence in Medical Affairs

    • Level of AI maturity – Total benchmark class
    • Level of AI maturity – Large pharma vs. Small-mid pharma
    • Using AI to enhance omnichannel engagement in Medical Affairs

    II. Personalized Engagement

    • Usage of AI for personalized engagement – Total benchmark class
    • Usage of AI for personalized engagement – Large pharma vs. Small-mid pharma
    • Valuable AI applications for the purposes of personalized engagement vs. AI applications providing the best insights for personalized engagement – Total benchmark class
    • Impact of AI for personalized engagement – Large pharma vs. Small-mid pharma
    • Value of insights from AI for personalized engagement – Large pharma vs. Small-mid pharma
    • Visualizing opportunities for artificial intelligence growth in personalized engagement

    III. Data Analysis & Evidence Generation

    • Usage of AI for big data analysis and evidence generation – Total benchmark class
    • Usage of AI for big data analysis and evidence generation – Large pharma vs. Small-mid pharma
    • Valuable AI applications for the purposes of big data analysis and evidence generation vs. AI applications providing the best insights for big data analysis and evidence generation – Total benchmark class
    • Impact of AI for big data analysis and evidence generation – Large pharma vs. Small-mid pharma
    • Value of insights from AI for big data analysis and evidence generation – Large pharma vs. Small-mid pharma
    • Visualizing opportunities for artificial intelligence growth in big data analysis and evidence generation

    IV. Strategy & Operations

    • Usage of AI for medical strategy and operations – Total benchmark class
    • Usage of AI for medical strategy and operations – Large pharma vs. Small-mid pharma
    • Valuable AI applications for the purposes of medical strategy and operations vs. AI applications providing the best insights for medical strategy and operations – Total benchmark class
    • Impact of AI for medical strategy and operations – Large pharma vs. Small-mid pharma
    • Value of insights from AI for medical strategy and operations – Large pharma vs. Small-mid pharma
    • Visualizing opportunities for artificial intelligence growth in medical strategy and operations

    V. Insights from AI Pilot Programs

    • Success stories from using artificial intelligence in benchmark Medical Affairs organizations
    • Success stories from using insights gathered from AI in benchmark Medical Affairs organizations
    • Top three lessons learned for developing and deploying artificial intelligence within a Medical Affairs organization
    • Areas of focus: Testimonials from Medical Affairs leaders at various stages of implementation highlight improvement areas to address for advancement of AI