Health workers are stepping into a world powered by artificial intelligence, yet the tools to measure their understanding of this technology are missing. A recent look at current training shows that most studies focus on medical and nursing students, leaving public health practice largely ignored. The research found that clear, theory-based definitions for AI literacy are rare, and connections to digital health literacy are only hinted at rather than proven.
To check how well people understand AI, researchers mostly used self-reported surveys. Common tools like the Artificial Intelligence Literacy Scale were used in a few studies, but many relied on custom-made questionnaires. Only one study out of twelve tried to measure algorithmic literacy separately. The skills being tested mostly covered basic awareness and ethics, while important communication skills were rarely checked.
This gap means we do not have a solid way to know if health workers are truly ready for AI-enabled tools. The findings suggest we need clearer definitions, better measurement methods, and a focus on the communication skills essential for patient care. As AI grows in healthcare, we must build systems that properly prepare the workforce without relying on shaky or incomplete data.
