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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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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>2026</year>
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					<volume>5</volume>
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				<issue>1</issue>
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					<title>A Multi-Source Public Health Surveillance Intelligence Mesh for Outbreak-Aware Systems</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Yousef</given_name>
            <surname>Al-Qahtani</surname>
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            <given_name>Fahad</given_name>
            <surname>Al-Salem</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Abdullah</given_name>
            <surname>Al-Harbi</surname>
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								<publication_date>
					<year>2026</year>
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					  <unstructured_citation>Mendes VIS, Pinto CS. Harnessing artificial intelligence for enhanced public health surveillance: a narrative review. Front Public Health. 2025;13:1601151.</unstructured_citation>
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					  <unstructured_citation>Idahor CO, Ogbuokiri B, Okoronkwo MC. Infectious disease surveillance in the era of big data and AI: opportunities and pitfalls. Cureus. 2025;17(1):e378889.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-f1440c83-6292-4eed-a305-8f7baa838b15">
					  <unstructured_citation>Omale LE, Ibiam VA, Sidikat LW, Taiwo O. Transformative applications of artificial intelligence in infectious disease forecasting and public health decision support systems. World J Adv Res Rev. 2025;25(3):2250-8.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-cf157772-61cf-463e-abc0-4239743223ea">
					  <unstructured_citation>El Morr C, Ozdemir D, Asdaah Y, Saab A, El-Lahib Y, Sokhn ES. AI-based epidemic and pandemic early warning systems: a systematic scoping review. Health Inform J. 2024;30(3):14604582241275844.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-7d7266fd-7e64-4247-9998-e84a8feb5a95">
					  <unstructured_citation>Syrowatka A, Kuznetsova M, Alpert A, Paradise SR, Greenwald P, Polinski JM, et al. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. NPJ Digit Med. 2021;4(1):96.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-b0874cfd-d748-4610-9208-a5fe9387697f">
					  <unstructured_citation>Zeng D, Cao Z, Neill DB. Artificial intelligence-enabled public health surveillance—from local detection to global epidemic monitoring and control. In: Artificial intelligence in medicine. Academic Press; 2021. p. 437-53.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-9f5f98b0-aa28-4017-9638-855bd45f0e73">
					  <unstructured_citation>Hao Y, Chen J, Wang X. Multimodal integration in health care: development with applications. Front Public Health. 2025;13:12370271.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-e33f692a-e581-417c-815f-d54393bdd045">
					  <unstructured_citation>Stella F. Learning from multiple data sources for decision making in health care. J Biomed Inform. 2025;153:104642.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v978476617-2e405a7f-7c7a-471c-8aa0-5708aa0c3c22">
					  <unstructured_citation>Nam Y, Kim J, Jung SH, Woerner J, Suh EH, Lee DG, et al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci. 2024;7(1):225-50.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-2cbff3e6-02d8-4b43-a97b-6d991b542239">
					  <unstructured_citation>Kitsios F, Kamariotou M. Recent advances of artificial intelligence in healthcare: a systematic literature review. Appl Sci. 2023;13(13):7479.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v978476617-1eabbcd9-641d-48f9-8ccc-7d4b6be5a545">
					  <unstructured_citation>Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119.</unstructured_citation>
						 <doi>10.1038/s41746-020-00323-1</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-16cc6ba8-85fc-4fa6-adb0-7275afe7200e">
					  <unstructured_citation>Tonekaboni S, Joshi S, McCradden MD, Goldenberg A. What clinicians want: contextualizing explainable machine learning for clinical end use. NPJ Digit Med. 2019;2:37.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-8d2be438-12d6-4c1a-9704-25a1e85b80f5">
					  <unstructured_citation>Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337-40.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-d9782630-518c-42d8-a23a-0f89dbb33882">
					  <unstructured_citation>Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58.</unstructured_citation>
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          					<citation key="rk-10.68159/v978476617-0e60c72d-784f-4a97-a8ae-23027c24ce0d">
					  <unstructured_citation>Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.</unstructured_citation>
						 <doi>10.1038/s41591-018-0300-7</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-bb78b115-4236-431f-8997-f742da6ff31b">
					  <unstructured_citation>Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020 ;369:m1328.</unstructured_citation>
						 <doi>10.1136/bmj.m1328</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-618004ca-efc5-4500-bf5c-b33b0fd2cacc">
					  <unstructured_citation>Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical machine learning in healthcare. Annu Rev Biomed Data Sci. 2021;4:123-44.</unstructured_citation>
						 <doi>10.1146/annurev-biodatasci-092820-114757</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-337fb4fb-c9a6-4fbe-9adb-ad3b7771b97d">
					  <unstructured_citation>Sendak MP, D’Arcy J, Kashyap S, Gao M, Nichols M, Corey K, et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020;4(1):29-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v978476617-23738361-c979-435c-a18a-34b00e7c6033">
					  <unstructured_citation>Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-8.</unstructured_citation>
						 <doi>10.7861/futurehosp.6-2-94</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-ab0e80a3-2bf7-4d4f-8549-ac780c8ef76d">
					  <unstructured_citation>Kaissis G, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2(6):305-11.</unstructured_citation>
						 <doi>10.1038/s42256-020-0186-1</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-3e05b960-0d6c-43e1-bf57-b575c42d002c">
					  <unstructured_citation>McLachlan S, Dube K, Gallagher T, et al. Developing a federated learning approach to predict adverse events in healthcare. J Biomed Inform. 2022;129:104045.</unstructured_citation>
						 <doi>10.1016/j.jbi.2022.104045</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-6c44b8e3-2176-49f8-b77e-656e9e771c09">
					  <unstructured_citation>Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record analysis. IEEE J Biomed Health Inform. 2018;22(5):1589-604.</unstructured_citation>
						 <doi>10.1109/JBHI.2017.2767063</doi> 					</citation>
          					<citation key="rk-10.68159/v978476617-39635e37-d4fd-4685-9381-1f8ff1d7a16a">
					  <unstructured_citation>Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236-46.</unstructured_citation>
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					  <unstructured_citation>Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.</unstructured_citation>
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					  <unstructured_citation>Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53.</unstructured_citation>
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