{"id":626,"date":"2026-03-23T09:56:35","date_gmt":"2026-03-23T08:56:35","guid":{"rendered":"https:\/\/www.meteda.it\/?p=626"},"modified":"2026-03-23T09:56:35","modified_gmt":"2026-03-23T08:56:35","slug":"ai-e-diabete","status":"publish","type":"post","link":"https:\/\/www.meteda.it\/en\/ai-e-diabete\/","title":{"rendered":"AI and Diabetes: Predictive Value Starts with Data Quality"},"content":{"rendered":"

Artificial intelligence can help anticipate risk signals in chronic diseases. In diabetology, however, its effectiveness depends on a often overlooked prerequisite: the quality of clinical data.<\/p>\n

Predictive capability is built on complete information collected over time: glycemic trends, therapies and their adjustments, comorbidities, intercurrent events, and longitudinal follow-up. Without this continuity, even the most advanced models provide partial or poorly contextualized results.<\/p>\n

For AI to be truly useful, a structured, traceable, and integrated information ecosystem is essential. This means that each data point must have a clear origin (who generated it, when, and in which clinical context), be properly recorded and protected from untracked changes, and not be duplicated across separate systems.<\/p>\n

It is also crucial that data collected remotely are integrated into the same electronic health record used in clinical practice, ensuring consistency in documentation and decision-making processes. Only a structured clinical record allows the connection of medical history, tests, therapies, and follow-up, transforming isolated data into meaningful clinical insights.<\/p>\n

Predictive medicine, therefore, is not a starting point, but the result of effective data governance. This is the foundation of Meteda\u2019s approach, focused on developing structured electronic health records and longitudinal clinical databases that support a more informed and continuous interpretation of clinical complexity.<\/p>","protected":false},"excerpt":{"rendered":"

L\u2019intelligenza artificiale pu\u00f2 aiutare ad anticipare segnali di rischio nelle malattie croniche. In diabetologia, tuttavia, la sua efficacia dipende da un presupposto spesso sottovalutato: la qualit\u00e0 del dato clinico. La predittivit\u00e0 si costruisce su informazioni complete e raccolte nel tempo: andamento glicemico, terapie e loro modifiche, comorbidit\u00e0, eventi intercorrenti e follow-up longitudinale. Senza questa continuit\u00e0, […]<\/p>","protected":false},"author":1,"featured_media":627,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[6,8],"tags":[],"class_list":["post-626","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diabete","category-intelligenza-artificiale"],"acf":[],"yoast_head":"\nAI e diabete: la predittivit\u00e0 nasce dalla qualit\u00e0 del dato | Meteda<\/title>\n<meta name=\"description\" content=\"L\u2019ai pu\u00f2 aiutare ad anticipare segnali di rischio nelle malattie croniche. 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