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	<title>Algorithmic gender bias &#8211; BIOENGINEER.ORG</title>
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	<title>Algorithmic gender bias &#8211; BIOENGINEER.ORG</title>
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		<title>Rethinking Gender Inference in Health Algorithms</title>
		<link>https://bioengineer.org/rethinking-gender-inference-in-health-algorithms/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 31 Dec 2025 14:29:34 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[AI fairness in medicine**]]></category>
		<category><![CDATA[Algorithmic gender bias]]></category>
		<category><![CDATA[and equity implications]]></category>
		<category><![CDATA[Based on the content focusing on algorithmic gender inference in EHR]]></category>
		<category><![CDATA[Electronic health records ethics]]></category>
		<category><![CDATA[here are 5 appropriate tags: **Gender inference in healthcare]]></category>
		<category><![CDATA[its ethical challenges]]></category>
		<category><![CDATA[limitations of binary classification]]></category>
		<category><![CDATA[Non-binary health data]]></category>
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					<description><![CDATA[In recent years, the integration of artificial intelligence and machine learning into healthcare has ushered in a new era of possibilities and challenges. One particularly controversial and thought-provoking area of this integration is the inference of gender from electronic health records (EHR). A study by Gronsbell et al., titled “When algorithms infer gender: revisiting computational [&#8230;]]]></description>
		
		
		
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