AI in Pharma: Opportunities and Challenges

Get ahead of the curve in this AI strategy masterclass for executives in Pharma.

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Course Overview

The applications of Artificial Intelligence (AI) to Pharma are rapidly building momentum. Following its remarkable feats in the finance and tech industries, AI is increasingly drawing attention in the pharmaceutical industry, where a foreseen capability to revolutionise drug development and clinical trial management for more personalised treatment has created fantastic incentives to adopt the technology.

The AI in Pharma think-tank will equip leaders in Pharma with strategic knowledge of the implementation and scaling of AI projects. Cutting through the hype, participants can expect to gain a practical understanding of the concrete steps for getting started with AI, of the most significant AI use cases, and of the likely developments in the Machine Learning space going forward.

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PRICE: TBC

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Why choose this course?

Why choose AI in Pharma?

  • Latest Use Case and Solution: Led by an expert in the field of Data Science, this C-level session will delve into the latest ML use cases, showing you the current and likely future applications of AI in Pharma.
  • Practical Impact: This exclusive one-day think-tank will give you the practical knowledge and tools to steer your organisation towards an AI-first future.
  • High-level Networking: Network with executives in this exclusive setting. You will benefit from working in small groups of senior participants – an unrivalled opportunity to benchmark and generate strategic thinking.

Who is it for?

  • Chief Information Officers
  • Chief Scientific Officers
  • Chief Digital Information Officers
  • Clinical Directors of Innovation
  • Directors of Bioinformatics
  • Heads of Artificial Intelligence
  • Chief Technology Officers
  • Senior VP and Chief Analytics Officers
  • Chief Operating Officers
  • Chief Development Officers

Programme Modules

  • Introduction and discussion of AI / Machine Learning
  • Case study analysis
  • Challenges in implementing AI solutions
  • Typical stages of an AI project
  • How to get started? How to get your organisation AI-ready

Learning outcomes

Benefits For Professionals

  • Acquire a high-level, up-to-date understanding of Machine Learning and its recent advances, with a specialised focus on ML applications in the pharmaceutical industry.
  • Know how to get started with AI, and show a nuanced understanding of the typical stages of an AI project lifecycle. Be able to generate forward-looking strategy.

Benefits For Your Organisation

  • Gain insights into critical ML use cases for pre-clinical development, clinical trial management, and late-stage portfolio optimisation – whilst understanding the challenges involved in implementation and scaling.
  • Understand the challenges involved in implementating AI in the healthcare and pharmaceutical industries

Programme Details

Module 1

Introduction and discussion of AI / Machine Learning

Focused on recent advances that are important to Pharma – what is happening and why is this relevant?

Module 2

Case study analysis

Investigating three critical use cases:

  • Personalised Treatment
  • Drug Development
  • Portfolio Optimisation

Module 3

Challenges in implementing AI solutions

Including:

  • Data Governance / Data Privacy / GDPR
  • Recruiting and Retaining Machine Learning Talent
  • Data Management – Data Being Held in Silos

Module 4

Typical stages of an AI project

  • Define the Problem / Use Case
  • Collect the Data
  • Train the AI Algorithm
  • Scale to Your Organisation

Module 5

How to get started? How to get your organisation AI-ready

Brainstorming: an exercise focused on building a roadmap for your organisation, and on assessing the next logical steps.

Speaker

Dmitry Kaminskiy

General Partner at Deep Knowledge Ventures

Dmitry Kaminskiy is an innovative entrepreneur and investor who is active in the fields of BioTech, FinTech, BlockChain, Artificial Intelligence and the intersection of these industries and technologies through their convergence.