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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
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					<year>2024</year>
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					<volume>4</volume>
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				<issue>2</issue>
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					<title>Generative AI in Clinical Workflows: Documentation Utility, Failure Modes, and Oversight Mechanisms</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Elena</given_name>
            <surname>Petrova</surname>
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            <given_name>Ivan</given_name>
            <surname>Georgiev</surname>
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					<year>2024</year>
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						 <doi>10.1038/s41746-023-00873-0</doi> 					</citation>
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          					<citation key="rk-10.68159/d070156303-c705e823-799c-4b4a-a417-c1edd9c7f20f">
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