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				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
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					<year>2025</year>
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					<volume>4</volume>
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				<issue>1</issue>
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					<title>Retrieval-Augmented Generation for Real-Time Clinical Question Answering: A Framework Integrating Electronic Health Records and Clinical Guidelines</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Mohammed</given_name>
            <surname>Al-Farsi</surname>
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            <given_name>Salim</given_name>
            <surname>Al-Harthy</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nasser</given_name>
            <surname>Al-Rawahi</surname>
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								<publication_date>
					<year>2025</year>
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					  <unstructured_citation>Jin Q, Dhingra B, Liu Z, Cohen W, Lu X. PubMedQA: a dataset for biomedical research question answering. In: Proc Conf Empir Methods Nat Lang Process Int Joint Conf Nat Lang Process (EMNLP-IJCNLP). 2019;2019:2567-77.</unstructured_citation>
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          					<citation key="rk-10.68159/d964147354-89079da0-b437-48df-b515-1de2e3e85c4c">
					  <unstructured_citation>Jin D, Pan E, Oufattole N, Weng WH, Fang H, Szolovits P, et al. What disease does this patient have? A large-scale open domain question answering dataset from medical exams. Appl Sci (Basel). 2021;11(14):6421.</unstructured_citation>
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					  <unstructured_citation>Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194.</unstructured_citation>
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          					<citation key="rk-10.68159/d964147354-8bf42e87-8820-4a2e-8d1e-b53f93674a4d">
					  <unstructured_citation>Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172-80.</unstructured_citation>
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          					<citation key="rk-10.68159/d964147354-45c98eef-4a93-499c-819d-b7ea2a3d044b">
					  <unstructured_citation>Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv Neural Inf Process Syst. 2020;33:9459-74.</unstructured_citation>
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					  <unstructured_citation>Karpukhin V, Oguz B, Min S, Lewis P, Wu L, Edunov S, et al. Dense passage retrieval for open-domain question answering. In: Proc Conf Empir Methods Nat Lang Process (EMNLP). 2020;2020:6769-81.</unstructured_citation>
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          					<citation key="rk-10.68159/d964147354-40fb5286-736b-40c8-80d0-9677c33d4343">
					  <unstructured_citation>Khattab O, Zaharia M. ColBERT: efficient and effective passage search via contextualized late interaction over BERT. In: Proc Int ACM SIGIR Conf Res Dev Inf Retr. 2020;2020:39-48.</unstructured_citation>
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          					<citation key="rk-10.68159/d964147354-c7983989-2e15-43a1-8dc0-bf0c5979918d">
					  <unstructured_citation>Pampari A, Raghavan P, Liang J, Peng J. emrQA: a large corpus for question answering on electronic medical records. In: Proc Conf Empir Methods Nat Lang Process. 2018;2018:2357-68.</unstructured_citation>
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					  <unstructured_citation>Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198.</unstructured_citation>
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					  <unstructured_citation>Shuster K, Poff S, Chen M, Kiela D, Weston J. Retrieval augmentation reduces hallucination in conversation. In: Findings Assoc Comput Linguist EMNLP 2021. 2021;2021:3784-803.</unstructured_citation>
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