{"id":3132,"date":"2023-11-02T08:29:41","date_gmt":"2023-11-02T08:29:41","guid":{"rendered":"https:\/\/caimed.de\/?page_id=3132"},"modified":"2024-06-06T07:39:39","modified_gmt":"2024-06-06T07:39:39","slug":"ki-wirkstoffe","status":"publish","type":"page","link":"https:\/\/caimed.de\/en\/forschung\/ki-wirkstoffe\/","title":{"rendered":"AI & Active Agents"},"content":{"rendered":"
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AI & Active Agents<\/h1>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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The use of AI and causal methods in pharmaceutical drug research and devleopment opens up diverse possibilities. Medications and active agents can be developed more efficiently. Techniques for orchestrating symbolic, causal reasoning, and machine learning models enhance the interpretability of AI-based solutions, drawing from medical knowledge found in specialized literature, ontologies, and databases. Moreover, mathematical models offer the potential to optimize the timing and dosage of medications specific to each patient. Through the use of a digital twin, which utilizes highly precise phenomenological models of complex biological processes to investigate personalized inquiries in a data-driven manner and develop new hypotheses, the search space in drug development can be reduced, thus advancing personalized medicine.<\/p>\n <\/div>\n <\/div>\n

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\n \n \n AI & Active Agents<\/span>\n
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Semantic Models<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Mathematical Models<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Digital Twins<\/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
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\n \n \n AI & Semantics<\/span>\n
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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
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\n \n \n AI & Decisions<\/span>\n
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Human-Centered AI<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Clinical Decision Support<\/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
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\n \n \n AI & Active Agents<\/span>\n
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Semantic Models<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Mathematical Models<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Digital Twins<\/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
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\n \n \n AI & Signals<\/span>\n
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Machine Learning<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Biomedical Image Recognition<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Collab. Development & Validation<\/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 & Wirkstoffe Der Einsatz von KI und kausalen Methoden in der pharmazeutischen Wirkstoffforschung er\u00f6ffnet vielf\u00e4ltige M\u00f6glichkeiten. Medikamente und Wirkstoffe k\u00f6nnen effizienter entwickelt werden. Techniken zur Orchestrierung von symbolischen, kausalen Schlussfolgerungen und maschinellen Lernmodellen erh\u00f6hen die Interpretierbarkeit von KI-basierten L\u00f6sungen, basierend auf medizinischem Wissen aus Fachliteratur, Ontologien und Datenbanken. Mathematische Modelle bieten zudem das Potenzial, […]<\/p>","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-3132","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3132","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=3132"}],"version-history":[{"count":4,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3132\/revisions"}],"predecessor-version":[{"id":8453,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3132\/revisions\/8453"}],"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=3132"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}