AI in pharma and life sciences

Automated digital data flow

When used to automate clinical trials, AI can significantly reduce cycle times and costs, while also improving the outcomes of clinical development. In fact, AI and machine learning are already being deployed by life sciences companies to automatically generate artifacts, such as, study protocols, and leverage natural language processing to accelerate manual tasks. AI algorithms, combined with an effective digital infrastructure, can also enable continuous streams of clinical trial data to be cleaned, aggregated, coded, stored, and managed. The big takeaway? Applications of AI could lead to faster, safer, and significantly less expensive clinical trials.

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Intelligent manufacturing and supply chain

Applying AI to biopharma manufacturing facilities and processes enables life sciences companies to stream factory and sensor data to analytics engines that generate novel insights. These insights can then help companies predict process bottlenecks, identify quality control issues, and proactively suggest corrective actions. Companies can also use AI to better predict demand and supply, recommend the next best action to supply chain operators, and even autonomously perform certain activities. Delivering on the grand vision of Biopharma 4.0, these “self-healing” supply chain tools can improve companies’ ability to dynamically respond to changes in market demand and supply availability, minimize disruptions, and improve decision-making related to product distribution and new product introductions.

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Personalized customer and patient engagement

Smarter, actionable insights. It’s what all biopharma commercial and brand leaders are after—and AI has the power to deliver. Patient journeys, advertising metrics, and HCP data can be combined and analyzed with AI to improve omnichannel marketing messaging and channels. In fact, dynamic personalization and engagement of HCPs can be customized based on insights from rich data sets and patterns. AI can even provide recommendations to marketing and sales reps on next best actions, channels, and personalized content. But the benefits of this approach stretch beyond marketing. It can also be used to analyze patient feedback and complaints, medical inquiries, and social media data to incorporate the voice of the patient into product iterations.

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Automated, evidence-based safety intelligence

Millions of adverse events are reported each year, and the volume is growing by about 10%- to 15% annually, compounded by data generated from new sources, such as wearables and social media. Processing these adverse event reports is a manual, costly, and often decentralized process that can jeopardize compliance with safety regulations and prevent early discovery. But automating and centralizing the intake of adverse event reports—leveraging AI-enabled technologies such as optical character recognition and natural language processing—can reduce case documentation workload to significantly expedite the investigation process. In fact, decreased manual effort can potentially lead to significant productivity savings, resulting in millions of dollars in savings.

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Intelligent threat intelligence, response, and recovery

The cyber threat landscape is growing exponentially. Insider threats are learning to evade signature-based systems, and bad actors are using AI to avoid detection by learning the most common detection rules. Smart cyber technologies will be increasingly needed to protect the growing volume of data biopharma companies own and can access. AI-based approaches can improve threat intelligence and prediction, and enable faster attack detection and response, augmenting efforts by cybersecurity experts.