From Design to Closure: Artificial Intelligence Transforming Clinical Research
Abstract
Clinical research is essential as it advances medical innovation, from developing new treatments and improving existing ones for additional disease indications to creating better processes and the availability of medical devices, yet traditional trial methods are often slow, costly, and full of challenges. Over the past decade, the use of artificial intelligence (AI) and machine learning (ML) has evolved across all phases of the clinical research cycle, from study design and planning to initiation, conduct, and closure. This editorial explores how AI can create new opportunities to enhance patient recruitment, optimize trial design, improve dose adherence and participant retention, strengthen safety monitoring, and enable advanced data analysis. It also highlights key challenges associated with the use of AI/ML, including selection bias, privacy, ethical considerations, and regulatory compliance. Since these tools generate outputs based on trained datasets, issues like data drift must be carefully managed to ensure ongoing accuracy and reliability. By recognizing both opportunities and challenges of using AI/ML across all stages of clinical research, we have proposed potential solutions to help overcome these challenges and promote responsible adoption of this new technological era. Responsible deployment and rigorous validation are essential; although hybrid approaches combine AI-driven insights with human oversight, these technologies can improve trial efficiency, improve…
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