<?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-jQNk-1790899041-b329996013</doi_batch_id>
		<timestamp>1790899041</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 Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2024</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Post-Deployment Update Triggers for Clinical AI: An Error-Taxonomy Framework for Safe Model Revision</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Luis</given_name>
            <surname>Herrera</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Daniela</given_name>
            <surname>Rojas</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Andres</given_name>
            <surname>Castro</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/b329996013</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/b329996013</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/b329996013-75cbfb94-c0fe-4dc3-b8f5-2b52692942ff">
					  <unstructured_citation>Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, et al. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med. 2022;5:66.</unstructured_citation>
						 <doi>10.1038/s41746-022-00611-y</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-2673d002-e09a-42c4-bfb8-c45e9e216471">
					  <unstructured_citation>Andersen ES, Birk-Korch JB, Hansen RS, Fly LH, Röttger R, Cespedes Arcani DM, et al. Monitoring performance of clinical artificial intelligence in health care: a scoping review. JBI Evid Synth. 2024;22(12):2423-46.</unstructured_citation>
						 <doi>10.11124/JBIES-24-00042</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-81849390-11b0-47ca-a788-0837aa0c5262">
					  <unstructured_citation>Davis SE, Embí PJ, Matheny ME. Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance. J Am Med Inform Assoc. 2024;31(5):1195-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b329996013-2c403af8-36ae-4445-ac36-e4cf067efb15">
					  <unstructured_citation>Koch LM, Baumgartner CF, Berens P. Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. NPJ Digit Med. 2024;7:120.</unstructured_citation>
						 <doi>10.1038/s41746-024-01085-w</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-321eb913-3799-43e9-bc73-3f0dd30bb12f">
					  <unstructured_citation>Davis SE, Walsh CG, Matheny ME. Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings. Front Digit Health. 2022;4:958284.</unstructured_citation>
						 <doi>10.3389/fdgth.2022.958284</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-4e3a6fd6-2c6d-4287-8fcf-3310de4a089b">
					  <unstructured_citation>Shick AA, Webber CM, Kiarashi N, Weinberg JP, Deoras A, Petrick N, et al. Transparency of artificial intelligence/machine learning-enabled medical devices. NPJ Digit Med. 2024;7(1):21.</unstructured_citation>
						 <doi>10.1038/s41746-023-00992-8</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-0e729ccc-33fe-4162-b537-44b68ef41bbf">
					  <unstructured_citation>Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283-6.</unstructured_citation>
						 <doi>10.1056/NEJMc2104626</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-ad971d30-14c4-4e4e-ba9d-12a1ae2b2932">
					  <unstructured_citation>Kore A, Abbasi Bavil E, Subasri V, Abdalla M, Fine B, Dolatabadi E, et al. Empirical data drift detection experiments on real-world medical imaging data. Nat Commun. 2024;15:1887.</unstructured_citation>
						 <doi>10.1038/s41467-024-46142-w</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-9974dd79-e2c7-4cb7-bf63-bbb513a0d545">
					  <unstructured_citation>Pruski M. Ethics framework for predictive clinical AI model updating. Ethics Inf Technol. 2023;25:48.</unstructured_citation>
						 <doi>10.1007/s10676-023-09721-x</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-fea78654-dff7-46f4-87f0-f6713efae2ec">
					  <unstructured_citation>Lennerz JK, Green U, Williamson DFK, Mahmood F. A unifying force for the realization of medical AI. NPJ Digit Med. 2022;5(1):172.</unstructured_citation>
						 <doi>10.1038/s41746-022-00721-7</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-6a44b4a1-fc43-40d7-b1e9-83c469dbc90a">
					  <unstructured_citation>Cabanillas Silva P, Sun H, Rezk M, Roccaro-Waldmeyer DM, Fliegenschmidt J, Hulde N, et al. Longitudinal model shifts of machine learning–based clinical risk prediction models: evaluation study of multiple use cases across different hospitals. J Med Internet Res. 2024;26:e51409.</unstructured_citation>
						 <doi>10.2196/51409</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-bc1798ba-bbaa-4419-b720-a6fdfc51d9be">
					  <unstructured_citation>Schiebinger L, Zou J. Ensuring that biomedical AI benefits diverse populations. EBioMedicine. 2021;67:103358.</unstructured_citation>
						 <doi>10.1016/j.ebiom.2021.103358</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-a2cdbee8-8458-4e17-86c3-58e5ea16231e">
					  <unstructured_citation>Labkoff S, Oladimeji B, Kannry J, Solomonides A, Leftwich R, Koski E, et al. Toward a responsible future: recommendations for AI-enabled clinical decision support. J Am Med Inform Assoc. 2024;31(11):2730-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b329996013-506178c8-a3db-40fb-9597-59ae1321ab26">
					  <unstructured_citation>Davis SE, Greevy RA, Fonnesbeck C, Lasko TA, Walsh CG, Matheny ME. A nonparametric updating method to correct clinical prediction model drift. J Am Med Inform Assoc. 2019;26(12):1448-57.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b329996013-7b64363f-847c-4978-a6ef-f6a95f4a3b40">
					  <unstructured_citation>Sahiner B, Chen W, Samala RK, Petrick N. Data drift in medical machine learning: implications and potential remedies. Br J Radiol. 2023;96(1150):20220878.</unstructured_citation>
						 <doi>10.1259/bjr.20220878</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-67acd38e-7da7-453f-8a34-502650e9e236">
					  <unstructured_citation>Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, et al. Continuous learning AI in radiology: implementation principles and early applications. Radiology. 2020;297(1):6-14.</unstructured_citation>
						 <doi>10.1148/radiol.2020200038</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-99b5721a-f058-415a-8512-fee8d20601a7">
					  <unstructured_citation>De Kerf G, Claessens M, Raouassi F, Mercier C, Stas D, Ost P, et al. A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer. Phys Imaging Radiat Oncol. 2023;28:100494.</unstructured_citation>
						 <doi>10.1016/j.phro.2023.100494</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-c77e7da3-dcfc-45d9-87e7-38b3ec1671a9">
					  <unstructured_citation>Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices. Artif Intell Med. 2024;152:102861.</unstructured_citation>
						 <doi>10.1016/j.artmed.2024.102861</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-342cc2c7-c3bf-49a6-b74f-523e8ff9c2b4">
					  <unstructured_citation>Muralidharan V, Adewale BA, Huang J,  Nta M, Ademiju PO, Pathmarajah P, et al. A scoping review of reporting gaps in FDA-approved AI medical devices. NPJ Digit Med. 2024;7:170.</unstructured_citation>
						 <doi>10.1038/s41746-024-01270-x</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-a2761b36-571e-4325-9bef-9c96b68f67db">
					  <unstructured_citation>Kale AU, Hogg HD, Pearson R, Glocker B, Golder S, Coombe A, et al. Detecting algorithmic errors and patient harms for AI-enabled medical devices in randomized controlled trials: protocol for a systematic review. JMIR Res Protoc. 2024;13:e51614.</unstructured_citation>
						 <doi>10.2196/51614</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-9baa47ef-988c-47d0-a21a-69feefcb7e71">
					  <unstructured_citation>Makridis CA, Mueller J, Tiffany T, Borkowski AA, Zachary J, Alterovitz G. From theory to practice: Harmonizing taxonomies of trustworthy AI. Health Policy Open. 2024;7:100128.</unstructured_citation>
						 <doi>10.1016/j.hpopen.2024.100128</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-aae2d02a-ed86-4a7b-93c0-e95e398ffcdb">
					  <unstructured_citation>Seo J, Choi D, Kim T, Cha WC, Kim M, Yoo H, et al. Evaluation Framework of Large Language Models in Medical Documentation: Development and Usability Study. J Med Internet Res. 2024;26:e58329.</unstructured_citation>
						 <doi>10.2196/58329</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-3489f686-9548-4d4c-b072-769e4101f864">
					  <unstructured_citation>Ramwala OA, Lowry KP, Hippe DS, Unrath MPN, Nyflot MJ, Mooney SD, et al. ClinValAI: A framework for developing Cloud-based infrastructures for the External Clinical Validation of AI in Medical Imaging. Pac Symp Biocomput. 2025;30:1-14.</unstructured_citation>
						 <doi>10.1142/9789819807024_0016</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-8c264c9d-76d8-4d5e-8501-ce5790a84b90">
					  <unstructured_citation>Workum JD, Meyfroidt G, Bakker J, Jung C, Tobin JM, Gommers D, et al. AI in critical care: A roadmap to the future. J Crit Care. 2026;91:155262.</unstructured_citation>
						 <doi>10.1016/j.jcrc.2025.155262</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-09a8bad0-fefc-424e-9b46-c2cf004e6631">
					  <unstructured_citation>Subasri V, et al. Diagnosing and remediating harmful data shifts for the responsible deployment of clinical AI models. medRxiv. 2024.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b329996013-08f1aed4-a578-4ee2-bdaf-b4fd0dae9c8d">
					  <unstructured_citation>Ng MY, Youssef A, Pillai M, Shah V, Hernandez-Boussard T. Scaling equitable artificial intelligence in healthcare with machine learning operations. BMJ Health Care Inform. 2024;31(1):e101101.</unstructured_citation>
						 <doi>10.1136/bmjhci-2024-101101</doi> 					</citation>
          					<citation key="rk-10.68159/b329996013-0fa7db57-f0a4-4693-9ce3-5012adaf65ce">
					  <unstructured_citation>Sittig DF, et al. Recommendations to ensure safety of AI in real-world clinical care. JAMA. 2024.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b329996013-766f3556-32e4-4ad5-88dd-b7a623f1e065">
					  <unstructured_citation>Maleki Varnosfaderani S, et al. The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering (Basel). 2024;11(4):337.</unstructured_citation>
						 <doi>10.3390/bioengineering11040337</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
