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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>2025</year>
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					<volume>5</volume>
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				<issue>1</issue>
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					<title>Artificial Intelligence for Healthcare Equity Analytics from 2017 to 2024: A Systematic Review of Bias Detection, Fairness-Aware Prediction, Disparity Monitoring, and Algorithmic Accountability in Health Systems</title>
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            <given_name>Khaled</given_name>
            <surname>Mahfouz</surname>
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            <given_name>Rania</given_name>
            <surname>Abdelaziz</surname>
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            <given_name>Sherif</given_name>
            <surname>Adel</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Dina</given_name>
            <surname>Mostafa</surname>
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            <given_name>Tamer</given_name>
            <surname>Nabil</surname>
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            <given_name>Reem</given_name>
            <surname>Saad</surname>
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					<year>2025</year>
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					  <unstructured_citation>Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866-72.</unstructured_citation>
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					  <unstructured_citation>Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. N Engl J Med. 2018;378(11):981-3.</unstructured_citation>
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