Applied intelligence in clinical drug development: Potential benefits and emerging concerns

Abstract

The use of artificial intelligence (AI) technology and machine learning (ML) is growing exponentially and is moving from AI to applied intelligence. Pharma industry is actively exploring the potential use of AI tools in new product discovery and clinical development. Some of the practical applications of AI in clinical development are for improving the efficiency of enrollment, selection and stratification of participants, optimizing study treatment, enhancing compliance, data analysis, and pharmacovigilance. AI applications have been used for outcome prediction; covariate selection/confounding adjustment; anomaly detection; real-world data phenotyping; imaging, video, and voice analysis; endpoint assessment; and pharmacometric modeling in regulatory submissions. However, widespread applications of novel yet difficult-to-understand AI technology in clinical development would need balancing the benefits and risks and resolving issues of scientific validity, technical quality, and ethics. The article discusses the potential benefits and emerging concerns of applying AI in clinical drug development.

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