{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T04:21:59Z","timestamp":1744258919387,"version":"3.40.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ophthalmologie"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title>\n          <jats:sec>\n            <jats:title>Hintergrund<\/jats:title>\n            <jats:p>Evidenzbasierte Therapieempfehlungen helfen bei der jeweiligen fachspezifischen Therapie, k\u00f6nnen aber Daten aus der Real-World-Versorgung kaum ber\u00fccksichtigen. Um diese im klinischen Alltag auch hinsichtlich pr\u00e4diktiver Aussagen zu Krankheitsprogression oder Behandlungserfolg besser zu nutzen, sind Modelle mit Daten aus der Versorgung zu entwickeln, um diese f\u00fcr die Schaffung von assistierender k\u00fcnstlicher Intelligenz zu nutzen.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Ziel<\/jats:title>\n            <jats:p>Ziel des Use Case\u00a01 innerhalb des MiHUBx (Medical Informatics Hub in Saxony) ist es, ein auf Versorgungs- und Forschungsdaten basierendes Modell f\u00fcr einen Biomarker-gest\u00fctzten Therapiealgorithmus sowie die dazu notwendige digitale Infrastruktur zu entwickeln.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Material und Methode<\/jats:title>\n            <jats:p>Schrittweise werden notwendige Partner:innen in Kliniken und Praxen technisch oder \u00fcber Forschungsfragen innerhalb des Use Case\u00a01 \u201eOphthalmologie trifft Diabetologie\u201c des regionalen Digitalen FortschrittsHub Gesundheit MiHUBx der bundesweiten Medizininformatik-Initiative zusammengeschlossen.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Ergebnisse<\/jats:title>\n            <jats:p>Basierend auf gemeinsamen Studien mit Diabetologen erfolgte die Auswahl robuster serologischer und bildgebender Biomarker, die Hinweise f\u00fcr eine Entwicklung eines diabetischen Makula\u00f6dems (DM\u00d6) geben. Diese und weitere wissenschaftlich nachgewiesene prognostische Marker sollen zuk\u00fcnftig in einen Therapiealgorithmus einflie\u00dfen, der KI(k\u00fcnstliche Intelligenz)-gest\u00fctzt ist. Daf\u00fcr werden gemeinsam mit Medizininformatikern modellhafte Vorgehensweisen erarbeitet sowie ein Datenintegrationszentrum etabliert.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Schlussfolgerungen<\/jats:title>\n            <jats:p>Neben der strukturierten und technischen Zusammenf\u00fchrung bisher an verschiedenen Orten vorliegender und teilweise heterogener Versorgungsdaten werden in dem Use Case die Chancen und H\u00fcrden zur Nutzung von Real-World-Daten zur Entwicklung k\u00fcnstlicher Intelligenz definiert.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00347-024-02146-x","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T07:54:50Z","timestamp":1734508490000},"page":"262-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MiHUBx: ein Digitaler FortschrittsHub zur Nutzung von intersektoralen klinischen Datens\u00e4tzen am Beispiel des diabetischen Makula\u00f6dems","MiHUBx: a\u00a0digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema"],"prefix":"10.1007","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9190-1139","authenticated-orcid":false,"given":"Gabriel","family":"Stolze","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinodh","family":"Kakkassery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danny","family":"Kowerko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Bartos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4765-0767","authenticated-orcid":false,"given":"Katja","family":"Hoffmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9888-8460","authenticated-orcid":false,"given":"Martin","family":"Sedlmayr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katrin","family":"Engelmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"2146_CR1","doi-asserted-by":"crossref","unstructured":"Alejandro Barredo Arrieta ND\u2011R, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2019) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. 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