ArticleNPJ digital medicine2026
Personalised health plan development using agentic AI in Singapore's national preventive care programme: a pilot study.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- User Acceptability and Adoption of AI-Generated Lifestyle Intervention Recommendations: Scoping Review and Theoretical Integration.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The workforce shortages caused by aging populations demand a transition from reactive to preventive healthcare strategies. Generative Artificial Intelligence offers a promising solution through the use of agents that can generate personalised guidance. We implement a digital assistant powered by a multi-agent framework that generates and refines personalised health plans based on user interactions. A pilot study with a cohort of 20 residents and 7 clinicians revealed positive user acceptance. Both groups rated four success metrics significantly above neutral satisfaction levels (p values: <0.05). The majority of residents valued the personalisation (p value: 0.003), appreciated the level of granularity (p value: 0.0003), and did not express major concerns about the recommended plans (p value: 0.941). More than 50% of the collected feedback reflected a positive sentiment on the personalised diet (p value: 0.110), personalised exercise (p value: 0.003), and general features (p value: 6e-06). This pilot study highlights the potential of AI-driven digital assistants in supporting preventive healthcare programmes.
Identifiers
What Socratic 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 Socratic graph.