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		<title>AI Reads the Trials: LLM Framework Speeds Systematic Reviews of Digital Health RCTs</title>
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		<pubDate>Sat, 10 Oct 2026 00:16:56 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[article screening]]></category>
		<category><![CDATA[clinical trial reporting]]></category>
		<category><![CDATA[CONSORT reporting guidelines]]></category>
		<category><![CDATA[Digital Health]]></category>
		<category><![CDATA[digital health equity]]></category>
		<category><![CDATA[evidence synthesis]]></category>
		<category><![CDATA[information extraction]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[Natural Language Processing]]></category>
		<category><![CDATA[PLOS Digital Health]]></category>
		<category><![CDATA[randomized controlled trials]]></category>
		<category><![CDATA[systematic reviews]]></category>
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					<description><![CDATA[Researchers show that an unmodified large language model can screen and extract data from digital health trial reports with human-level agreement, pointing toward faster systematic reviews.]]></description>
		
		
		
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