ReviewFrontiers in endocrinology2025
Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges.
Review in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Pharmacists in modern primary care and management of diseases: an evidence-based perspective on emerging technologies.BioImpacts : BI · 2026Article
- Technology-driven diabetes care: innovation without equity?Frontiers in digital health · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (GenAI) is transforming public health and medicine as well, in the form of disease surveillance, resource allocation and clinical decision making. Interventions to improve efficiency - multimodal predictive algorithms, federated learning platforms - reveal the internal contradictions of the system between algorithmic efficiency and fairness: speed of technical innovation and regulatory deficit, data flows without borders vs. ethical values of places. We present a three-dimensional governance structure for the topic covering the technical, institutional and ethical domains. From a technology point of view, explainability solutions and culturally-aware design align transparency with cultural sensibility. From an institution point of view, privacy-protecting data platforms and risk-based regulation align innovation with accountability. From an ethical point of view, incorporating local values and disbursing AI dividends sustain equitable health outcomes. There are still challenges that demand the utmost priority, including the algorithmic prejudice, the data imperialism and the opacity in medical AI decision making. Future priorities include the development of broader measurement tools that integrate clinical impact, equity, and societal impact; the development of transnational governance institutions to mitigate concerns relating to data sovereignty; and the development of forms of participatory design between designers, practitioners, and populations. A balance between technical creativity, visionary policy-making, and caring leadership to advocate for human-centered healthcare will provide us with trusted AI ecosystems. Technical excellence alone cannot guarantee success unless fairness and accessibility, social responsiveness, and justice for future global health is guaranteed.
Indexed as
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.