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Increased Adoption of Artificial Intelligence (AI) and Machine Learning

Q1. In what areas of pharma/biopharma are you most excited to see increased adoption of artificial intelligence (AI) and machine learning?

Raj Indupuri, Chief Executive Officer, eClinical Solutions

There are many opportunities for AI/ML across the entire clinical trials value chain, but in terms of adoption what’s most exciting right now is seeing AI/ML applications that can help us scale and manage some of the most challenging and time-consuming facets of managing the clinical data in trials. Everyone across the industry is looking for cycle time improvements and efficiency gains. Clinical data is the currency of life sciences, but, historically, manual methods have persisted within the data processes of clinical development. As the volumes of data being collected from patients grew, it became unscalable to manage those data without applying advanced approaches in data science like ML models or AI. Now we are in a place where interest and intent in AI is being matched with heightened adoption of approaches that are available today. Using AI models, we can automate data review to identify data quality issues and augment the work of data reviewers so that they can identify some of the issues that would have taken significant time otherwise. AI can detect outliers and data issues and quickly surface these as outputs that a data reviewer — the “human-in-the-loop,” — can address and act on, removing the need to manually address these incredible numbers of individual data points. Applying AI to these clinical data use cases will create efficiencies and decrease cycle times across clinical trials.

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2024 Industry Outlook: Driving Tomorrow’s Breakthroughs with Clinical Data Transformation