Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges

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 inte...

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Vydáno v:Frontiers in endocrinology (Lausanne) Ročník 16; s. 1620132
Hlavní autoři: Deng, Mengqi, Yang, Ruiye, Zheng, Xiaoran, Deng, Yaoqi, Jiang, Junyi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 19.11.2025
Témata:
algorithmic fairness
Algorithms
Artificial Intelligence - ethics
Artificial Intelligence - trends
Clinical Decision-Making
data colonialism
Diabetes Mellitus - diagnosis
Diabetes Mellitus - therapy
explainable AI
generative artificial intelligence
Humans
medical AI governance
public health informatics
public health informatics
medical AI governance
ethical machine learning
algorithmic fairness
health equity
explainable AI
data colonialism
generative artificial intelligence
ISSN:1664-2392, 1664-2392
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Shrnutí: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.
ISSN:1664-2392
1664-2392
DOI:10.3389/fendo.2025.1620132