<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>cirpublications.com-3oSI-1790877874-i254334718</doi_batch_id>
		<timestamp>1790877874</timestamp>
		<depositor>
			<depositor_name>Clinical Intelligence Research Press</depositor_name>
			<email_address>info@cirpublications.com</email_address>
		</depositor>
		<registrant>Clinical Intelligence Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2024</year>
				</publication_date>
				<journal_volume>
					<volume>3</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Deep Multimodal Diagnostic Fusion of Medical Imaging and Structured Clinical Data</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Pham Quang</given_name>
            <surname>Minh</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Le Thi</given_name>
            <surname>Bich</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Nguyen Thanh</given_name>
            <surname>Huy</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/i254334718</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/i254334718</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/i254334718-0453d682-277d-40db-a436-5b102ac96776">
					  <unstructured_citation>Qinhong D, Huang X, Zhao Y, Li Y, Wang W, Feng Y. MAS-Net: multi-modal assistant segmentation network for lumbar intervertebral disc. J Biomed Inform. 2023;145:104465.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/i254334718-3a951c4f-f64c-4f7c-b607-3d7a64c8eb32">
					  <unstructured_citation>Xiong J, Li H, Ma R, Fang D, Zhao Z, Li F, et al. Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology. 2022;129(2):171-80.</unstructured_citation>
						 <doi>10.1016/j.ophtha.2021.07.032</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-270109b9-57da-48a0-82c0-bd5d806ec1b2">
					  <unstructured_citation>Liu X, Wu D, Sun B, Tan L, Chen J, Chen W. A deep learning model for classification of parotid neoplasms based on multimodal magnetic resonance image sequences. Laryngoscope. 2023;133(2):327-35.</unstructured_citation>
						 <doi>10.1002/lary.30219</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-a6dd2802-e0cf-4fda-99bc-651ed2dee769">
					  <unstructured_citation>Kihara Y, Heinke Diaz B, Gupta RR, Lee AY, Li F, Zhou B, et al. Policy-driven, multimodal deep learning for predicting visual fields from the optic disc and OCT imaging. Ophthalmology. 2022;129(7):781-91.</unstructured_citation>
						 <doi>10.1016/j.ophtha.2022.02.017</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-2bed0e00-d247-4434-8625-4de6f44bcf64">
					  <unstructured_citation>Dar SUH, Öztürk Ş, Taşlı T, Gudbjartsson T, Lienhart R, Riegler MA. Parallel-stream fusion of scan-specific and scan-general priors for learning deep MRI reconstruction in low-data regimes. Med Image Anal. 2023;90:102968.</unstructured_citation>
						 <doi>10.1016/j.media.2023.102968</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-2e7d1d83-e36e-4c13-a049-e2e3a5174334">
					  <unstructured_citation>Maqsood S, Damaševičius R, Maskeliūnas R. Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare. Neural Netw. 2023;160:244-58.</unstructured_citation>
						 <doi>10.1016/j.neunet.2023.01.022</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-9874efc6-5255-4e48-934a-ebbaf1578e1a">
					  <unstructured_citation>Tutty MA, Carlasare LE, Vakharia N, Jagsi R, Hertz N. The complex case of EHRs: examining the factors impacting the EHR user experience. J Am Med Inform Assoc. 2019;26(7):673-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/i254334718-85a3f24d-0b92-4651-8dc6-d7214371a9de">
					  <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/i254334718-015b990e-edb8-4560-9cca-7aabaad362dc">
					  <unstructured_citation>Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-8.</unstructured_citation>
						 <doi>10.1038/s41591-021-01614-0</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-e3deeece-b30b-418e-b8fa-50989afde217">
					  <unstructured_citation>Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, et al. Foundation models for generalist medical artificial intelligence. Nature. 2023;616(7956):259-65. doi:10.1038/s41586-023-05881-4</unstructured_citation>
						 <doi>10.1038/s41586-023-05881-4</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-7c6e8047-315c-4528-9a24-7e43fd0e6e57">
					  <unstructured_citation>Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9. doi:10.1038/s41591-018-0316-z</unstructured_citation>
						 <doi>10.1038/s41591-018-0316-z</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-4a755354-7541-48a2-af4d-a98d9b5a9ff6">
					  <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):62-70</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/i254334718-c1e12fdc-da9c-466d-bad5-33258cf4f0c2">
					  <unstructured_citation>Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75. doi:10.1136/bjophthalmol-2018-313173</unstructured_citation>
						 <doi>10.1136/bjophthalmol-2018-313173</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-ee1591e0-2c4a-4f1f-93eb-6f645c738d0b">
					  <unstructured_citation>Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48.</unstructured_citation>
						 <doi>10.1146/annurev-bioeng-071516-044442</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-cdfb78a2-219a-436d-aceb-da45d70f4301">
					  <unstructured_citation>Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals. Lancet Digit Health. 2019;1(6):e271-e297.</unstructured_citation>
						 <doi>10.1016/S2589-7500(19)30123-2</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-0549ddcc-0676-4b98-b0d1-51dfde146375">
					  <unstructured_citation>Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: a decision support tool for surgical team selection. Artif Intell Med. 2017;76:16-6.</unstructured_citation>
						 <doi>10.1016/j.artmed.2017.02.002</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-2fa73385-9ec0-4041-8cec-d50aa10d4168">
					  <unstructured_citation>He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of AI technologies in medicine. Nat Med. 2019;25(1):30-6.</unstructured_citation>
						 <doi>10.1038/s41591-018-0307-0</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-fa92ee2f-5e7b-4aeb-a7a3-03e78ef383c2">
					  <unstructured_citation>Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with AI. BMC Med. 2019;17:195.</unstructured_citation>
						 <doi>10.1186/s12916-019-1426-2</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-f8e9f8ca-0cbe-4ac6-9617-2a027c526887">
					  <unstructured_citation>Cheung CY, Ran AR, Wang S, Chan VTT, Sham K, Chan KS, et al. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health. 2022;4(11):e806-e815.</unstructured_citation>
						 <doi>10.1016/S2589-7500(22)00169-8</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-4cfa6034-2465-45d2-ad8f-b4bbfd731ba2">
					  <unstructured_citation>Jiang Y, Wang Z, Jin M, Wang X, Chen D, Wang W, et al. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. Lancet Digit Health. 2022;4(5):e340-e350.</unstructured_citation>
						 <doi>10.1016/S2589-7500(22)00040-1</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-6508cb0a-05f7-4ee3-8bf6-5a5b17844d09">
					  <unstructured_citation>Willer K, Fingerle AA, Noichl W, De Marco F, Frank M, Urban T, et al. X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study. Lancet Digit Health. 2021;3(11):e733-e744.</unstructured_citation>
						 <doi>10.1016/S2589-7500(21)00146-1</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-99fe68be-1b1f-471a-8b0a-a8db786e8b1e">
					  <unstructured_citation>Hiremath A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, et al. An integrated nomogram combining deep learning, PI-RADS scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. Lancet Digit Health. 2021;3(7):e445-e454.</unstructured_citation>
						 <doi>10.1016/S2589-7500(21)00082-0</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-98c12461-5030-4bb4-8f66-6144884b99e7">
					  <unstructured_citation>Forte JC, Badrigilan S, Borges LR, Feijóo RA, Attié S. Comorbidities and medical history essential for mortality prediction in critically ill patients. The Lancet Digital Health. 2019;1(2):e67-e68.</unstructured_citation>
						 <doi>10.1016/S2589-7500(19)30030-5</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-90d672c4-6a16-4261-abbc-883a53d85bae">
					  <unstructured_citation>Choy WJ, Parr WCH, Mao Y. Current state of 3D-printed custom-made spinal implants. The Lancet Digital Health. 2019;1(5):e206-e207.</unstructured_citation>
						 <doi>10.1016/S2589-7500(19)30081-0</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-e24e0020-9797-4374-8eb2-9bbbfc004414">
					  <unstructured_citation>Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2:305-11.</unstructured_citation>
						 <doi>10.1038/s42256-020-0186-1</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-606bc789-bf64-4aec-8bf1-8bdf594ad3aa">
					  <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/i254334718-58dafe4a-56cc-45a9-a94f-fc6328da727a">
					  <unstructured_citation>Tang R, Yang X, Li Y, Liu H, Li L, Liu S, et al. Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study. Lancet Digit Health. 2023;5(4):e185-e193.</unstructured_citation>
						 <doi>10.1016/S2589-7500(23)00026-2</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-8a55da80-0565-455f-afb7-d851ab6bb1d0">
					  <unstructured_citation>Ishii-Rousseau JE, Hara T, Jagannathan S, Fumihara T, Shimizu S, Yanagisawa T, et al. The ecosystem as a service (EaaS) approach to advance clinical artificial intelligence. PLOS Digit Health. 2022;1(2):e0000011.</unstructured_citation>
						 <doi>10.1371/journal.pdig.0000011</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-d0c76f94-228b-4b30-8da6-b0a988b306fe">
					  <unstructured_citation>Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Ideal algorithms in healthcare: explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS Digit Health. 2022;1(1):e0000006.</unstructured_citation>
						 <doi>10.1371/journal.pdig.0000006</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-907e74f4-33b4-42f1-9148-e6fa6bf937b0">
					  <unstructured_citation>Betzler BK, Rim TH, Sabanayagam C, Cheng CY. Large language models and their impact in ophthalmology. Lancet Digit Health. 2023;5(12):e917-e924.</unstructured_citation>
						 <doi>10.1016/S2589-7500(23)00201-7</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-0c7ddb1f-e4ea-480c-a917-734d62ce0370">
					  <unstructured_citation>Williams GJ, Al-Busaidi A, Raniga S, Maharaj V, Vyas R, Carr MW. Wearable technology and the cardiovascular system: the future of patient assessment. Lancet Digit Health. 2023;5(7):e467-e476.</unstructured_citation>
						 <doi>10.1016/S2589-7500(23)00087-0</doi> 					</citation>
          					<citation key="rk-10.68159/i254334718-a64275cb-f021-48c3-beb1-78250fe1066d">
					  <unstructured_citation>Syrowatka A, Song W, Amato MG, Foer D, Edrees M, Seger DL, et al. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. Lancet Digit Health. 2022;4(2):e137-e148.</unstructured_citation>
						 <doi>10.1016/S2589-7500(21)00229-6</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
