Clinical Researcher

This article examines how artificial intelligence (AI) can supplement human expertise to optimize clinical trial protocols and improve study completion rates by targeting subject recruitment and retention. The integration of AI represents not a replacement of human judgment but a critical bridge—linking innovation with the efficiency and adaptability that modern clinical research urgently requires.

Increasingly complex and costly clinical trials mean significant increases in workload for frontline clinical research staff such as clinical research coordinators (CRCs). For CRCs, this means higher cognitive load, increased per-patient workload, and a greater probability of errors that can compromise both patient safety and data integrity. This commentary illustrates how the CRC role necessitates a more data-driven and strategic function, while simultaneously preserving core competencies in patient care and clinical judgment. Furthermore, it identifies existing gaps and potential areas for future exploration regarding the practical implementation of artificial intelligence (AI) tools within clinical research workflows.

Artificial intelligence (AI) is rapidly transforming clinical research, yet its successful adoption depends on aligning technological innovation with workforce readiness and site-level realities. This article applies the Technology Acceptance Model as a theoretical foundation to examine how clinical research coordinators and clinical research associates perceive the integration of AI into their workflows. It also examines the role of AI literacy as a determinant of preparedness for technology-driven workflows.

With an eye toward designing a practical artificial intelligence (AI) architecture for clinical data review (among other goals), this literature review summarizes findings about AI techniques, statistical surveillance, and the regulation of clinical data review. It compares generative AI, natural language processing, discriminatory models, and anomaly detection with reinforcement learning and outlines the purposes of validation and regulation.

Life sciences organizations have made progress in connecting data and processes across the product lifecycle, in part for preparation to launch artificial intelligence (AI) use cases. The focus on streamlining operations is quickly shifting to create even greater data flow through connected execution across stakeholders.