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	<title>Pathology foundation models &#8211; BIOENGINEER.ORG</title>
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		<title>Pathology Models Adaptation Boosts Fairness, Generalization</title>
		<link>https://bioengineer.org/pathology-models-adaptation-boosts-fairness-generalization/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 12 Dec 2025 22:10:46 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Cross-domain generalization]]></category>
		<category><![CDATA[Demographic fairness in AI]]></category>
		<category><![CDATA[Knowledge-guided adaptation]]></category>
		<category><![CDATA[Medical AI advancements]]></category>
		<category><![CDATA[Pathology foundation models]]></category>
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					<description><![CDATA[In the rapidly evolving landscape of artificial intelligence and medical diagnostics, a groundbreaking study has emerged that promises to transform how pathology models are adapted and deployed across diverse clinical settings. Researchers Huang, Zhao, Zhang, and their collaborators have introduced an innovative framework that leverages knowledge-guided adaptation of pathology foundation models, a method designed not [&#8230;]]]></description>
		
		
		
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