Clinical chemists urged to lead the data-driven future of healthcare

Laboratory leaders are urging the profession to evolve to meet the demands of an increasingly data-driven era in healthcare. Their warning: clinical chemistry must claim its place at the center of the AI revolution or risk being sidelined by it.

For the American Chamber of Health's members, this is a story about where clinical value is migrating. Laboratories sit on the structured data that AI needs most, and the Chamber's ecosystem includes the diagnostics innovators, health IT leaders, and providers turning that data into decisions. The question Bayoumi poses is one American laboratory leaders should be asking now: will labs lead the intelligence revolution or be sidelined by it?

In a session titled "Smart Labs, smart patients: Reinventing clinical chemistry for the future," Riad Bayoumi, Professor and Head of Clinical Chemistry at Mohammed Bin Rashid University of Medicine and Health Sciences, outlined how artificial intelligence, continuous patient monitoring, and integrated data systems are redefining the role of clinical chemistry within modern hospitals.

Bayoumi highlighted how traditional laboratory testing has historically relied on isolated measurements ("snapshots") rather than continuous physiological data. With AI and continuous monitoring gaining traction, he argued, clinical chemistry is uniquely positioned to underpin future decision-support systems. "If you look at the lab, we are sitting on 75% of the information that will help a clinician to make a decision. Therefore, the position of a clinical chemist should be at the centre of decision making within a hospital, not simply someone who is offering figures and numbers for a clinician to think about."

That positioning matters because laboratories already operate within highly automated, integrated workflows, generating consistent quantitative outputs. That is precisely the structured data required for advanced machine learning and predictive modeling. Bayoumi cautioned, however, that laboratories must actively engage with the technological shift, or risk losing their strategic role, as external AI teams, expanding IT departments, and technology providers enter healthcare.

He also pointed to the growing role of digital twins in healthcare, where virtual patient models simulate clinical scenarios, anticipate risks, and personalize treatment strategies. These approaches rely heavily on time-series quantitative data, where clinical chemistry already plays a foundational role.

Vendors are already moving in this direction: AxisOne's AI-powered R&D platform combines laboratory discovery with predictive timelines and performance benchmarking, while Genes2Me's end-to-end automated molecular diagnostics systems point to the same future: the laboratory as a computational discipline.