{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T09:06:30Z","timestamp":1743757590493},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1016\/j.artmed.2023.102560","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:17:56Z","timestamp":1682468276000},"page":"102560","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review"],"prefix":"10.1016","volume":"141","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7765-5430","authenticated-orcid":false,"given":"Khaled M.","family":"Toffaha","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1555-5012","authenticated-orcid":false,"given":"Mecit Can Emre","family":"Simsekler","sequence":"additional","affiliation":[]},{"given":"Mohammed Atif","family":"Omar","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.artmed.2023.102560_b1","article-title":"Avoidability of hospital deaths and association with hospital-wide mortality ratios: Retrospective case record review and regression analysis","volume":"351","author":"Hogan","year":"2015","journal-title":"BMJ (Online)"},{"key":"10.1016\/j.artmed.2023.102560_b2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1136\/bmjqs-2018-008053","article-title":"Rate of avoidable deaths in a Norwegian hospital trust as judged by retrospective chart review","volume":"28","author":"Rogne","year":"2019","journal-title":"BMJ Qual Saf"},{"key":"10.1016\/j.artmed.2023.102560_b3","article-title":"Prevalence, severity, and nature of preventable patient harm across medical care settings: Systematic review and meta-analysis","volume":"366","author":"Panagioti","year":"2019","journal-title":"BMJ"},{"key":"10.1016\/j.artmed.2023.102560_b4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1097\/PTS.0b013e3182948a69","article-title":"A new, evidence-based estimate of patient harms associated with hospital care","volume":"9","author":"James","year":"2013","journal-title":"J Patient Saf"},{"key":"10.1016\/j.artmed.2023.102560_b5","article-title":"Medical error-the third leading cause of death in the US","volume":"353","author":"Makary","year":"2016","journal-title":"BMJ (Online)"},{"key":"10.1016\/j.artmed.2023.102560_b6","series-title":"Medical error","author":"Carver N","year":"2023"},{"key":"10.1016\/j.artmed.2023.102560_b7","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1136\/bmj.39551.680417.C2","article-title":"US hospitals pass on most of the costs of errors","volume":"336","author":"Tanne","year":"2008","journal-title":"BMJ (Clin Res Ed)"},{"key":"10.1016\/j.artmed.2023.102560_b8","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.jbusres.2021.04.070","article-title":"How to conduct a bibliometric analysis: An overview and guidelines","volume":"133","author":"Donthu","year":"2021","journal-title":"J Bus Res"},{"key":"10.1016\/j.artmed.2023.102560_b9","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: Vosviewer, a computer program for bibliometric mapping","volume":"84","author":"van Eck","year":"2010","journal-title":"Scientometrics"},{"key":"10.1016\/j.artmed.2023.102560_b10","series-title":"Patient safety and quality: An evidence-based handbook for nurses","author":"Hughes RG","year":"2008"},{"key":"10.1016\/j.artmed.2023.102560_b11","series-title":"Healthcare cost and utilization project (HCUP) statistical briefs","year":"2006"},{"key":"10.1016\/j.artmed.2023.102560_b12","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1111\/iwj.13071","article-title":"The national cost of hospital-acquired pressure injuries in the United States","volume":"16","author":"Padula","year":"2019","journal-title":"Int Wound J"},{"issue":"3","key":"10.1016\/j.artmed.2023.102560_b13","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1097\/WON.0000000000000327","article-title":"Relationship of wound, ostomy, and continence certified nurses and healthcare-acquired conditions in acute care hospitals","volume":"44","author":"Boyle","year":"2017","journal-title":"J Wound, Ostomy, Cont Nurs : Official Publ Wound Ostomy Cont Nurs Soc"},{"key":"10.1016\/j.artmed.2023.102560_b14","series-title":"Prevention and treatment of pressure ulcers: Quick reference guide disclaimer","year":"2009"},{"issue":"4","key":"10.1016\/j.artmed.2023.102560_b15","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1097\/00006199-198707000-00002","article-title":"The braden scale for predicting pressure sore risk","volume":"36","author":"Bergstrom","year":"1987","journal-title":"Nurs Res"},{"issue":"3","key":"10.1016\/j.artmed.2023.102560_b16","first-page":"24","article-title":"Calculating the risk. Reflections on the norton scale","volume":"2","author":"Norton","year":"1989","journal-title":"Adv Skin Wound Care"},{"issue":"48","key":"10.1016\/j.artmed.2023.102560_b17","first-page":"49","article-title":"Pressure sores: a risk assessment card","volume":"81","author":"Waterlow","year":"1985","journal-title":"Nurs Times"},{"key":"10.1016\/j.artmed.2023.102560_b18","doi-asserted-by":"crossref","DOI":"10.3390\/informatics8040076","article-title":"Literature review of machine-learning algorithms for pressure ulcer prevention: Challenges and opportunities","volume":"8","author":"Ribeiro","year":"2021","journal-title":"Informatics"},{"key":"10.1016\/j.artmed.2023.102560_b19","doi-asserted-by":"crossref","DOI":"10.2196\/25704","article-title":"Using machine learning technologies in pressure injury management: systematic review","volume":"9","author":"Jiang","year":"2021","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.artmed.2023.102560_b20","doi-asserted-by":"crossref","first-page":"198977","DOI":"10.1109\/ACCESS.2020.3035327","article-title":"Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: A systematic review","volume":"8","author":"Tulloch","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.artmed.2023.102560_b21","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ijnurstu.2018.08.005","article-title":"Evaluating the development and validation of empirically-derived prognostic models for pressure ulcer risk assessment: A systematic review","volume":"89","author":"Shi","year":"2019","journal-title":"Int J Nurs Stud"},{"key":"10.1016\/j.artmed.2023.102560_b22","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.ijmedinf.2015.05.013","article-title":"Approaches that use software to support the prevention of pressure ulcer: A systematic review","volume":"84","author":"Marchione","year":"2015","journal-title":"Int J Med Inform"},{"key":"10.1016\/j.artmed.2023.102560_b23","first-page":"955","article-title":"E-health decision support technologies in the prevention and management of pressure ulcers: A systematic review","volume":"39","author":"Ting","year":"2021","journal-title":"CIN"},{"key":"10.1016\/j.artmed.2023.102560_b24","doi-asserted-by":"crossref","DOI":"10.2196\/21621","article-title":"Clinical decision support systems for pressure ulcer management: Systematic review","volume":"8","author":"Araujo","year":"2020","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.artmed.2023.102560_b25","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"10.1016\/j.artmed.2023.102560_b26","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1136\/qshc.2005.015362","article-title":"Prediction of pressure ulcer development in hospitalized patients: a tool for risk assessment","volume":"15","author":"Schoonhoven","year":"2006","journal-title":"Qual Saf Health Care"},{"key":"10.1016\/j.artmed.2023.102560_b27","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1111\/j.1365-2753.2010.01417.x","article-title":"Friction and shear highly associated with pressure ulcers of residents in long-term care - Classification Tree Analysis (CHAID) of Braden items","volume":"17","author":"Lahmann","year":"2011","journal-title":"J Eval Clin Pract"},{"key":"10.1016\/j.artmed.2023.102560_b28","doi-asserted-by":"crossref","DOI":"10.1186\/s12911-017-0471-z","article-title":"Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks","volume":"17","author":"Kaewprag","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.artmed.2023.102560_b29","doi-asserted-by":"crossref","first-page":"43","DOI":"10.4258\/hir.2017.23.1.43","article-title":"Applying of decision tree analysis to risk factors associated with pressure ulcers in long-term care facilities","volume":"23","author":"Moon","year":"2017","journal-title":"Healthc Inform Res"},{"key":"10.1016\/j.artmed.2023.102560_b30","first-page":"248","article-title":"Predictability of pressure ulcers based on operation duration, transfer activity, and body mass index through the use of an alternating decision tree","volume":"63","author":"Setoguchi","year":"2016","journal-title":"JMI"},{"key":"10.1016\/j.artmed.2023.102560_b31","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1097\/WON.0000000000000388","article-title":"Artificial neural network: A method for prediction of surgery-related pressure injury in cardiovascular surgical patients","volume":"45","author":"Chen","year":"2018","journal-title":"J Wound, Ostomy, Cont Nurs : Official Publ Wound, Ostomy Cont Nurs Soc"},{"issue":"1","key":"10.1016\/j.artmed.2023.102560_b32","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph20010828","article-title":"An integrated system of multifaceted machine learning models to predict if and when hospital-acquired pressure injuries (bedsores) occur","volume":"20","author":"Dweekat","year":"2023","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"10.1016\/j.artmed.2023.102560_b33","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13010031","article-title":"A hybrid system of braden scale and machine learning to predict hospital-acquired pressure injuries (Bedsores): A retrospective observational cohort study","volume":"13","author":"Dweekat","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.artmed.2023.102560_b34","doi-asserted-by":"crossref","DOI":"10.3389\/fmedt.2022.926667","article-title":"Machine learning approaches for hospital acquired pressure injuries: A retrospective study of electronic medical records","volume":"4","author":"Levy","year":"2022","journal-title":"Front Med Technol"},{"key":"10.1016\/j.artmed.2023.102560_b35","first-page":"147","article-title":"Learning Bayesian networks for the prediction of unfavorable health events in nursing homes","volume":"294","author":"Charon","year":"2022","journal-title":"Stud Health Technol Inform"},{"issue":"7","key":"10.1016\/j.artmed.2023.102560_b36","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1111\/iwj.13764","article-title":"Development and validation of a machine learning algorithm\u2013based risk prediction model of pressure injury in the intensive care unit","volume":"19","author":"Xu","year":"2022","journal-title":"Int Wound J"},{"issue":"1","key":"10.1016\/j.artmed.2023.102560_b37","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-09050-x","article-title":"Prediction of inpatient pressure ulcers based on routine healthcare data using machine learning methodology","volume":"12","author":"Walther","year":"2022","journal-title":"Sci Rep"},{"issue":"4","key":"10.1016\/j.artmed.2023.102560_b38","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics12040850","article-title":"Machine learning-based pressure ulcer prediction in modular critical care data","volume":"12","author":"\u0160\u00edn","year":"2022","journal-title":"Diagnostics"},{"issue":"10","key":"10.1016\/j.artmed.2023.102560_b39","first-page":"659","article-title":"Explainable artificial intelligence for predicting hospital-acquired pressure injuries in COVID-19\u2013positive critical care patients","volume":"40","author":"Alderden","year":"2022","journal-title":"CIN"},{"key":"10.1016\/j.artmed.2023.102560_b40","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1111\/jan.14680","article-title":"A model for predicting 7-day pressure injury outcomes in paediatric patients: A machine learning approach \u2013 7","volume":"77","author":"Chun","year":"2021","journal-title":"J Adv Nurs"},{"key":"10.1016\/j.artmed.2023.102560_b41","doi-asserted-by":"crossref","DOI":"10.1186\/s12911-020-01371-z","article-title":"Hospital acquired pressure injury prediction in surgical critical care patients","volume":"21","author":"Alderden","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.artmed.2023.102560_b42","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.3390\/ijerph18062954","article-title":"Identifying the risk factors associated with nursing home residents\u2019 pressure ulcers using machine learning methods","volume":"18","author":"Lee","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"10.1016\/j.artmed.2023.102560_b43","series-title":"Proceedings - 2021 IEEE 9th international conference on healthcare informatics","first-page":"427","article-title":"Machine learning approaches for pressure injury prediction","author":"Ahmad","year":"2021"},{"key":"10.1016\/j.artmed.2023.102560_b44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-021-01608-5","article-title":"Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence","volume":"21","author":"Anderson","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"10.1016\/j.artmed.2023.102560_b45","first-page":"759","article-title":"Predicting pressure injury using nursing assessment phenotypes and machine learning methods","volume":"28","author":"Song","year":"2021","journal-title":"JAMIA"},{"key":"10.1016\/j.artmed.2023.102560_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijnurstu.2021.103932","article-title":"Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan","volume":"119","author":"Nakagami","year":"2021","journal-title":"Int J Nurs Stud"},{"key":"10.1016\/j.artmed.2023.102560_b47","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.2147\/RMHP.S297838","article-title":"The random forest model has the best accuracy among the four pressure ulcer prediction models using machine learning algorithms","volume":"14","author":"Song","year":"2021","journal-title":"Risk Manag Healthc Policy"},{"key":"10.1016\/j.artmed.2023.102560_b48","first-page":"415","article-title":"Constructing inpatient pressure injury prediction models using machine learning techniques","volume":"38","author":"Hu","year":"2020","journal-title":"CIN"},{"key":"10.1016\/j.artmed.2023.102560_b49","article-title":"Predicting the development of surgery-related pressure injury using a machine learning algorithm model","volume":"29","author":"Cai","year":"2020","journal-title":"JNR"},{"key":"10.1016\/j.artmed.2023.102560_b50","first-page":"49","article-title":"Predicting the incidence of pressure ulcers in the intensive care unit using machine learning","volume":"7","author":"Cramer","year":"2019","journal-title":"EGEMS (Washington, DC)"},{"key":"10.1016\/j.artmed.2023.102560_b51","doi-asserted-by":"crossref","first-page":"461","DOI":"10.4037\/ajcc2018525","article-title":"Predicting pressure injury in critical care patients: A machine-learning model","volume":"27","author":"Alderden","year":"2018","journal-title":"Am J Crit Care : An Official Publ, Am Assoc Crit-Care Nurs"},{"key":"10.1016\/j.artmed.2023.102560_b52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/wound.2018.0803","article-title":"Prediction of in-hospital pressure ulcer development","volume":"8","author":"Cichosz","year":"2019","journal-title":"Adv Wound Care"},{"issue":"9","key":"10.1016\/j.artmed.2023.102560_b53","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.ijmedinf.2011.06.009","article-title":"Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly","volume":"80","author":"Fossum","year":"2011","journal-title":"Int J Med Inform"},{"key":"10.1016\/j.artmed.2023.102560_b54","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/j.ijmedinf.2013.05.009","article-title":"Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study","volume":"82","author":"Fossum","year":"2013","journal-title":"Int J Med Inform"},{"key":"10.1016\/j.artmed.2023.102560_b55","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1097\/MLR.0000000000000080","article-title":"Evaluation of AHRQ\u2019s on-time pressure ulcer prevention program: A facilitator-assisted clinical decision support intervention for nursing homes","volume":"52","author":"Olsho","year":"2014","journal-title":"Med Care"},{"key":"10.1016\/j.artmed.2023.102560_b56","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ijmedinf.2011.10.008","article-title":"Development and evaluation of data entry templates based on the entity-attribute-value model for clinical decision support of pressure ulcer wound management","volume":"81","author":"Kim","year":"2012","journal-title":"Int J Med Inform"},{"key":"10.1016\/j.artmed.2023.102560_b57","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1097\/WON.0b013e31826a4b5c","article-title":"Improving accuracy of pressure ulcer staging and documentation using a computerized clinical decision support system","volume":"39","author":"Alvey","year":"2012","journal-title":"J Wound, Ostomy Cont Nurs"},{"key":"10.1016\/j.artmed.2023.102560_b58","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1111\/j.1365-2524.2007.00728.x","article-title":"Using the discrete choice experimental design to investigate decision-making about pressure ulcer prevention by community nurses","volume":"15","author":"Papanikolaou","year":"2007","journal-title":"Health Soc Care Community"},{"key":"10.1016\/j.artmed.2023.102560_b59","article-title":"Development and effectiveness of a clinical decision support system for pressure ulcer prevention care using machine learning: A quasi-experimental study","author":"Kim","year":"2022","journal-title":"CIN"},{"key":"10.1016\/j.artmed.2023.102560_b60","first-page":"1","article-title":"Visualization of guideline-based decision support for the management of pressure ulcers in nursing homes","volume":"275","author":"Abdellatif","year":"2020","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.artmed.2023.102560_b61","doi-asserted-by":"crossref","DOI":"10.3390\/informatics4030020","article-title":"Modeling the construct of an expert evidence-adaptive knowledge base for a pressure injury clinical decision support system","volume":"4","author":"Khong","year":"2017","journal-title":"Informatics"},{"key":"10.1016\/j.artmed.2023.102560_b62","first-page":"15","article-title":"Potential of decision support in preventing pressure ulcers in hospitals","volume":"241-A","author":"Wang","year":"2017","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.artmed.2023.102560_b63","doi-asserted-by":"crossref","first-page":"251","DOI":"10.4338\/ACI-2012-12-RA-0056","article-title":"Enhancement of decision rules to increase generalizability and performance of the rule-based system assessing risk for pressure ulcer","volume":"4","author":"Choi","year":"2013","journal-title":"Appl Clin Inform"},{"key":"10.1016\/j.artmed.2023.102560_b64","doi-asserted-by":"crossref","first-page":"508","DOI":"10.4338\/ACI-2011-07-RA-0046","article-title":"Evaluation of the pressure ulcer prevention clinical decision report for bedside nurses in acute care hospitals","volume":"2","author":"Talsma","year":"2011","journal-title":"Appl Clin Inform"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S093336572300074X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S093336572300074X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T10:10:26Z","timestamp":1714212626000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S093336572300074X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":64,"alternative-id":["S093336572300074X"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2023.102560","relation":{},"ISSN":["0933-3657"],"issn-type":[{"value":"0933-3657","type":"print"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2023.102560","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102560"}}