ReviewNature human behaviour2026
Building health-literate artificial intelligence.
Review in Nature human behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This Perspective asks whether systems mediated by artificial intelligence (AI) can support health communication and health literacy rather than shifting interpretive burden onto users. We define health-literate AI as AI designed to align information, guidance and responsibility with users' abilities, contexts and needs. We propose four interrelated components: comprehension, agency, accountability and proportionality. These components help distinguish systems that are merely accurate or explainable from systems designed to support users in understanding what matters, what uncertainties remain and what to do next. We argue that the principles of health-literate AI should inform design, evaluation, research and governance across health-relevant AI ecosystems, so that innovation supports understanding, informed action and institutional responsibility.
Identifiers
42745006What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.