AI in Pharma: Benefits, Risks, and the Road Ahead

Introduction

Use of artificial intelligence (AI) now permeates every industry, and pharma is no exception. Whether machine learning (ML) models that make predictions based on existing data or generative AI (GenAI) models that create new data based on the data they were trained on, AI is being used to streamline and accelerate each step of the drug development process from research through approval and marketing.

According to McKinsey & Co., generative AI alone could produce $60 billion to $110 billion a year in economic value across the pharma industry value chain. And $13 billion to $25 billion of that annual value alone would be for clinical development.¹

AI is able to handle both structured and unstructured data, including multimodal data such as tabular, text, images, and videos. At its most basic level, AI can automate mundane tasks such as structured document and image analyses, enabling experts to spend more time on tasks that require their attention and proficiency.

On a deeper level, AI can unveil insights from historical data to inform operations and provide a lens into the future through predictive analytics, supplementing traditional descriptive and diagnostic analytics that solely provide analytical information anchored on historical patterns. It can also enable and accelerate expertise through prescriptive analytics, advising experts on the next best action to take to maximize added value.²

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1. McKinsey & Co. (2024) Generative AI in the pharmaceutical industry: Moving from hype to reality. Available from https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-movingfrom-hype-to-reality#/ (Accessed Aug. 21, 2024)
2. Jaspersoft (2024) What is Prescriptive Analytics? Available from https://www.jaspersoft.com/articles/what-is-prescriptiveanalytics (Accessed Aug. 21, 2024)