{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:05:56Z","timestamp":1780549556454,"version":"3.54.1"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,3]]},"DOI":"10.7759\/cureus.110205","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:32:33Z","timestamp":1780547553000},"source":"Crossref","is-referenced-by-count":0,"title":["A Clinical-Operational Framework for Responsible Artificial Intelligence Use in Infection Risk Management Among Immunosuppressed Patients: A Technical Report"],"prefix":"10.7759","author":[{"given":"Alejandro","family":"Rodriguez Bobadilla","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio Alonso","family":"Bobadilla Ocampo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingrid Carolina","family":"Bobadilla Ocampo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.4514","article-title":"Development and validation of a machine learning model to estimate bacterial sepsis among immunocompromised recipients of stem cell transplant","volume":"4","author":"Lind ML","year":"2021","unstructured":"Lind ML, Mooney SJ, Carone M, et al.. Development and validation of a machine learning model to estimate bacterial sepsis among immunocompromised recipients of stem cell transplant. JAMA Netw Open. 2021, 4:e214514. 10.1001\/jamanetworkopen.2021.4514","journal-title":"JAMA Netw Open"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar A","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019, 380:1347-58. 10.1056\/NEJMra1814259","journal-title":"N Engl J Med"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(23)00221-2","article-title":"Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance","volume":"6","author":"Howard A","year":"2024","unstructured":"Howard A, Aston S, Gerada A, et al.. Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance. Lancet Digit Health. 2024, 6:79-86. 10.1016\/S2589-7500(23)00221-2","journal-title":"Lancet Digit Health"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1128\/cmr.00179-21","article-title":"Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective","volume":"35","author":"Kim JI","year":"2022","unstructured":"Kim JI, Maguire F, Tsang KK, et al.. Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective. Clin Microbiol Rev. 2022, 35:e0017921. 10.1128\/cmr.00179-21","journal-title":"Clin Microbiol Rev"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"377","author":"Vasey B","year":"2022","unstructured":"Vasey B, Nagendran M, Campbell B, et al.. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022, 377:e070904. 10.1136\/bmj-2022-070904","journal-title":"BMJ"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir K","year":"2025","unstructured":"Lekadir K, Frangi AF, Porras AR, et al.. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025, 388:e081554. 10.1136\/bmj-2024-081554","journal-title":"BMJ"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2021.2626","article-title":"External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients","volume":"181","author":"Wong A","year":"2021","unstructured":"Wong A, Otles E, Donnelly JP, et al.. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021, 181:1065-70. 10.1001\/jamainternmed.2021.2626","journal-title":"JAMA Intern Med"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00611-y","article-title":"Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare","volume":"5","author":"Feng J","year":"2022","unstructured":"Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, Pirracchio R. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med. 2022, 5:66. 10.1038\/s41746-022-00611-y","journal-title":"NPJ Digit Med"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01794-w","article-title":"Responsible artificial intelligence governance in oncology","volume":"8","author":"Stetson PD","year":"2025","unstructured":"Stetson PD, Choy J, Summerville N, et al.. Responsible artificial intelligence governance in oncology. NPJ Digit Med. 2025, 8:407. 10.1038\/s41746-025-01794-w","journal-title":"NPJ Digit Med"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-01894-0","article-title":"Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis","volume":"28","author":"Adams R","year":"2022","unstructured":"Adams R, Henry KE, Sridharan A, et al.. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nat Med. 2022, 28:1455-60. 10.1038\/s41591-022-01894-0","journal-title":"Nat Med"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03933-y","article-title":"Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis","volume":"31","author":"Liesenfeld O","year":"2025","unstructured":"Liesenfeld O, Arora S, Aufderheide TP, et al.. Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis. Nat Med. 2025, 31:4044-54. 10.1038\/s41591-025-03933-y","journal-title":"Nat Med"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.landig.2025.01.010","article-title":"Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study","volume":"7","author":"Ming DK","year":"2025","unstructured":"Ming DK, Vasikasin V, Rawson TM, Georgiou P, Davies FJ, Holmes AH, Hernandez B. Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study. Lancet Digit Health. 2025, 7:e205-15. 10.1016\/j.landig.2025.01.010","journal-title":"Lancet Digit Health"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0213-5","article-title":"The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care","volume":"24","author":"Komorowski M","year":"2018","unstructured":"Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018, 24:1716-20. 10.1038\/s41591-018-0213-5","journal-title":"Nat Med"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/501302-a-clinical-operational-framework-for-responsible-artificial-intelligence-use-in-infection-risk-management-among-immunosuppressed-patients-a-technical-report","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:32:48Z","timestamp":1780547568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/501302-a-clinical-operational-framework-for-responsible-artificial-intelligence-use-in-infection-risk-management-among-immunosuppressed-patients-a-technical-report"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":13,"URL":"https:\/\/doi.org\/10.7759\/cureus.110205","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6,3]]}}}