MINI REVIEW article
Front. Endocrinol.
Sec. Clinical Diabetes
This article is part of the Research TopicSmart Dietary Management for Precision Diabetes Mellitus CareView all 8 articles
Artificial Intelligence in Diabetes Care: From Predictive Analytics to Generative AI and Implementation Challenges
Provisionally accepted
- Capital Medical University, Beijing, China
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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 can not guarantee success unless fairness and accessibility, social responsiveness, and justice for future global health is guaranteed.
Keywords: Generative artificial intelligence, Public Health Informatics, Medical AI Governance, Algorithmic Fairness, Explainable AI, data colonialism, health equity, Ethical machine learning
Received: 30 Apr 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Li, Zheng, Deng and Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mengqi Deng, 112021040261@mail.ccmu.edu.cn
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