Pierre de Malliard

In healthcare and life sciences, AI agents help organizations process clinical data, submit regulatory filings, automate medical coding, and accelerate drug development and commercialization. However, the sensitive nature of healthcare data and regulatory requirements like Good Practice (GxP) compliance require human oversight at key decision points. This is where human-in-the-loop (HITL) constructs become essential.

This post is co-authored with the Biomni group from Stanford. Biomedical researchers spend approximately 90% of their time manually processing massive volumes of scattered information. This is evidenced by Genentech’s challenge of processing 38 million biomedical publications in PubMed, public repositories like the Human Protein Atlas, and their internal repository of hundreds of millions of cells across hundreds of diseases.

Healthcare and life sciences organizations are transforming drug discovery, medical devices, and patient care with generative AI agents. In regulated industries, any system that impacts product quality or patient safety must comply with GxP (Good Practice) regulations, such as Good Clinical Practice (GxP), Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP). Organizations must demonstrate to regulatory authorities that their AI agents are safe, effective, and meet quality standards.