{"id":3130,"date":"2023-11-02T08:29:40","date_gmt":"2023-11-02T08:29:40","guid":{"rendered":"https:\/\/caimed.de\/?page_id=3130"},"modified":"2026-01-27T07:48:53","modified_gmt":"2026-01-27T06:48:53","slug":"ki-semantik","status":"publish","type":"page","link":"https:\/\/caimed.de\/en\/forschung\/ki-semantik\/","title":{"rendered":"AI & Semantics"},"content":{"rendered":"
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AI & Semantics<\/h1>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Analyzing large volumes of medical data for clinical decision-making requires not only the identification of statistical relationships but also an understanding of causal relationships in biological processes. The use of causal and data-driven models makes these analyses more robust and easier to understand. Machine learning methods bridge the gap between clinical information, imaging, laboratory test results, and electronic health records to provide a holistic view of patients. The development of evidence synthesis methods that integrate heterogeneous data sources and are based on counterfactual methods, meta-analysis, and artificial neural networks shows great promise. The formation of early-career research groups on the topics of AI and Causality, AI and Bioinformatics, AI Systems in Multi-Omics Data, and Statistical Evidence in AI Systems is intended to tap into the potential in the field of \u201cAI and Semantics.\u201d<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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\n \n \n AI & Semantics<\/span>\n
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Trustworthy AI & Causality<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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AI & Bioinformatics<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Multi-Omics Data<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Statistical Evidence<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

KI & Semantik Die Analyse umfangreicher medizinischer Daten f\u00fcr klinische Entscheidungen erfordert nicht nur das Erkennen statistischer Beziehungen, sondern auch das Verst\u00e4ndnis kausaler Zusammenh\u00e4nge in biologischen Prozessen. Die Nutzung von kausalen und datengetriebenen Modellen macht diese robuster und verst\u00e4ndlicher. Methoden des maschinellen Lernens schlagen eine Br\u00fccke zwischen klinischen Informationen, Bildgebung, Laborwerten und elektronischen Gesundheitsakten f\u00fcr […]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":2907,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"footnotes":""},"class_list":["post-3130","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3130","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/comments?post=3130"}],"version-history":[{"count":7,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3130\/revisions"}],"predecessor-version":[{"id":14348,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3130\/revisions\/14348"}],"up":[{"embeddable":true,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/2907"}],"wp:attachment":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/media?parent=3130"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}