{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T19:37:22Z","timestamp":1785267442590,"version":"3.55.0"},"reference-count":71,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001230","name":"Macquarie University","doi-asserted-by":"publisher","award":["iMQRES 20201869"],"award-info":[{"award-number":["iMQRES 20201869"]}],"id":[{"id":"10.13039\/501100001230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"NHMRC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Centre for Research Excellence","award":["APP1134919"],"award-info":[{"award-number":["APP1134919"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and methods<\/jats:title>\n                  <jats:p>We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n\u2009=\u200912) and risk assessment (n\u2009=\u20099). The majority of studies examined assistive CDS (n\u2009=\u200923) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocad180","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T22:17:10Z","timestamp":1693433830000},"page":"2050-2063","source":"Crossref","is-referenced-by-count":43,"title":["Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review"],"prefix":"10.1093","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5155-6904","authenticated-orcid":false,"given":"Anindya Pradipta","family":"Susanto","sequence":"first","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University , Sydney, NSW 2109, Australia"},{"name":"Faculty of Medicine, Universitas Indonesia , Jakarta, DKI Jakarta 10430, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2695-0368","authenticated-orcid":false,"given":"David","family":"Lyell","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University , Sydney, NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bambang","family":"Widyantoro","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Universitas Indonesia , Jakarta, DKI Jakarta 10430, Indonesia"},{"name":"National Cardiovascular Center Harapan Kita Hospital , Jakarta, DKI Jakarta 11420, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shlomo","family":"Berkovsky","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University , Sydney, NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8426-5588","authenticated-orcid":false,"given":"Farah","family":"Magrabi","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University , Sydney, NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"2023111709555286100_ocad180-B1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1201\/b13617","volume-title":"Guide to Health Informatics","author":"Coiera","year":"2015","edition":"3rd ed"},{"key":"2023111709555286100_ocad180-B2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success","volume":"3","author":"Sutton","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"10","key":"2023111709555286100_ocad180-B3","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat Biomed Eng"},{"issue":"2","key":"2023111709555286100_ocad180-B4","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/S1470-2045(18)30835-0","article-title":"On algorithms, machines, and medicine","volume":"20","author":"Coiera","year":"2019","journal-title":"Lancet Oncol"},{"issue":"3","key":"2023111709555286100_ocad180-B5","article-title":"Association of clinician diagnostic performance with machine learning-based decision support systems: a systematic review","volume":"4","author":"Vasey","year":"2021","journal-title":"JAMA Netw"},{"issue":"1","key":"2023111709555286100_ocad180-B6","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1055\/s-0039-1677903","article-title":"Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications","volume":"28","author":"Magrabi","year":"2019","journal-title":"Yearb Med Inform"},{"issue":"3","key":"2023111709555286100_ocad180-B7","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1136\/amiajnl-2011-000094","article-title":"Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings","volume":"18","author":"Jaspers","year":"2011","journal-title":"J Am Med Inform Assoc"},{"key":"2023111709555286100_ocad180-B8","doi-asserted-by":"crossref","first-page":"m3216","DOI":"10.1136\/bmj.m3216","article-title":"Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials","volume":"370","author":"Kwan","year":"2020","journal-title":"BMJ"},{"issue":"3","key":"2023111709555286100_ocad180-B9","doi-asserted-by":"crossref","first-page":"118","DOI":"10.11622\/smedj.2022044","article-title":"Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications","volume":"63","author":"Li","year":"2022","journal-title":"Singapore Med J"},{"key":"2023111709555286100_ocad180-B10","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1111\/1754-9485.13193","article-title":"Artificial intelligence in clinical decision support and outcome prediction - applications in stroke","volume":"65","author":"Yeo","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"key":"2023111709555286100_ocad180-B11","doi-asserted-by":"crossref","first-page":"665464","DOI":"10.3389\/fmed.2021.665464","article-title":"Artificial intelligence for clinical decision support in sepsis","volume":"8","author":"Wu","year":"2021","journal-title":"Front Med (Lausanne)"},{"issue":"11","key":"2023111709555286100_ocad180-B12","doi-asserted-by":"crossref","first-page":"e16323","DOI":"10.2196\/16323","article-title":"The last mile: where artificial intelligence meets reality","volume":"21","author":"Coiera","year":"2019","journal-title":"J Med Internet Res"},{"issue":"1","key":"2023111709555286100_ocad180-B13","doi-asserted-by":"crossref","DOI":"10.1136\/bmjhci-2020-100301","article-title":"How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices","volume":"28","author":"Lyell","year":"2021","journal-title":"BMJ Health Care Inform"},{"key":"2023111709555286100_ocad180-B14","first-page":"35","article-title":"Assessing technology success and failure using information value chain theory","volume":"263","author":"Coiera","year":"2019","journal-title":"Stud Health Technol Inform"},{"issue":"4","key":"2023111709555286100_ocad180-B15","doi-asserted-by":"crossref","first-page":"e25759","DOI":"10.2196\/25759","article-title":"Role of artificial intelligence applications in real-life clinical practice: systematic review","volume":"23","author":"Yin","year":"2021","journal-title":"J Med Internet Res"},{"issue":"4","key":"2023111709555286100_ocad180-B16","doi-asserted-by":"crossref","first-page":"e12286","DOI":"10.2196\/12286","article-title":"Applications of machine learning in real-life digital health interventions: review of the literature","volume":"21","author":"Triantafyllidis","year":"2019","journal-title":"J Med Internet Res"},{"issue":"1","key":"2023111709555286100_ocad180-B17","doi-asserted-by":"crossref","first-page":"e28639","DOI":"10.2196\/28639","article-title":"Human factors and technological characteristics influencing the interaction of medical professionals with artificial intelligence-enabled clinical decision support systems: literature review","volume":"9","author":"Knop","year":"2022","journal-title":"JMIR Hum Factors"},{"issue":"1","key":"2023111709555286100_ocad180-B18","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1080\/1364557032000119616","article-title":"Scoping studies: towards a methodological framework","volume":"8","author":"Arksey","year":"2005","journal-title":"Int J Soc Res Methodol"},{"issue":"69","key":"2023111709555286100_ocad180-B19","first-page":"1","article-title":"Scoping studies: advancing the methodology","volume":"5","author":"Levac","year":"2010","journal-title":"Implement Sci"},{"issue":"7","key":"2023111709555286100_ocad180-B20","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"2023111709555286100_ocad180-B21","article-title":"Joanna Briggs Institute Reviewers' Manual","author":"Peters","year":"2015"},{"key":"2023111709555286100_ocad180-B22","author":"The World Bank","year":"2021"},{"issue":"3","key":"2023111709555286100_ocad180-B23","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/3468.844354","article-title":"A model for types and levels of human interaction with automation","volume":"30","author":"Parasuraman","year":"2000","journal-title":"IEEE Trans Syst Man Cybern B"},{"issue":"1","key":"2023111709555286100_ocad180-B24","first-page":"251","article-title":"A new informatics geography","author":"Coiera","year":"2016","journal-title":"Yearb Med Inform"},{"issue":"1","key":"2023111709555286100_ocad180-B25","doi-asserted-by":"crossref","first-page":"e2032320","DOI":"10.1001\/jamanetworkopen.2020.32320","article-title":"Effect of machine learning on dispatcher recognition of out-of-hospital cardiac arrest during calls to emergency medical services: a randomized clinical trial","volume":"4","author":"Blomberg","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"5","key":"2023111709555286100_ocad180-B26","first-page":"1035","article-title":"Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: a pilot usability study","volume":"165","author":"Brennan","year":"2019","journal-title":"Surgery (United States)"},{"issue":"1","key":"2023111709555286100_ocad180-B27","doi-asserted-by":"crossref","DOI":"10.1136\/bmjhci-2019-100109","article-title":"Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals","volume":"27","author":"Burdick","year":"2020","journal-title":"BMJ Health Care Inform"},{"issue":"11","key":"2023111709555286100_ocad180-B28","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1097\/CCM.0000000000003891","article-title":"A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice","volume":"47","author":"Giannini","year":"2019","journal-title":"Crit Care Med"},{"issue":"11","key":"2023111709555286100_ocad180-B29","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1097\/CCM.0000000000003803","article-title":"Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock","volume":"47","author":"Ginestra","year":"2019","journal-title":"Crit Care Med"},{"issue":"4","key":"2023111709555286100_ocad180-B30","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S2468-1253(19)30413-3","article-title":"Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study","volume":"5","author":"Gong","year":"2020","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"2023111709555286100_ocad180-B31","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s12911-016-0268-5","article-title":"Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial","volume":"16","author":"Grigull","year":"2016","journal-title":"BMC Med Inform Decis Mak"},{"issue":"4","key":"2023111709555286100_ocad180-B32","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/s10916-021-01727-6","article-title":"Technology acceptance of a machine learning algorithm predicting delirium in a clinical setting: a mixed-methods study","volume":"45","author":"Jauk","year":"2021","journal-title":"J Med Syst"},{"issue":"9","key":"2023111709555286100_ocad180-B33","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1093\/jamia\/ocaa113","article-title":"Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study","volume":"27","author":"Jauk","year":"2020","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2023111709555286100_ocad180-B34","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4103\/sjg.SJG_377_19","article-title":"Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy","volume":"26","author":"Liu","year":"2020","journal-title":"Saudi J Gastroenterol"},{"issue":"6","key":"2023111709555286100_ocad180-B35","doi-asserted-by":"crossref","first-page":"357","DOI":"10.7326\/M18-0249","article-title":"Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy a prospective study","volume":"169","author":"Mori","year":"2018","journal-title":"Ann Intern Med"},{"issue":"4","key":"2023111709555286100_ocad180-B36","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.brachy.2020.03.004","article-title":"Conventional vs machine learning-based treatment planning in prostate brachytherapy: results of a Phase I randomized controlled trial","volume":"19","author":"Nicolae","year":"2020","journal-title":"Brachytherapy"},{"issue":"4","key":"2023111709555286100_ocad180-B37","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1093\/jac\/dky514","article-title":"Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study","volume":"74","author":"Rawson","year":"2019","journal-title":"J Antimicrob Chemother"},{"issue":"2","key":"2023111709555286100_ocad180-B38","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1053\/j.gastro.2020.04.062","article-title":"Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial","volume":"159","author":"Repici","year":"2020","journal-title":"Gastroenterology"},{"key":"2023111709555286100_ocad180-B39","doi-asserted-by":"crossref","first-page":"104072","DOI":"10.1016\/j.ijmedinf.2019.104072","article-title":"A lesson in implementation: a pre-post study of providers' experience with artificial intelligence-based clinical decision support","volume":"137","author":"Romero-Brufau","year":"2020","journal-title":"Int J Med Inform"},{"issue":"1","key":"2023111709555286100_ocad180-B40","doi-asserted-by":"crossref","DOI":"10.1186\/s13014-020-01528-0","article-title":"Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy","volume":"15","author":"Savenije","year":"2020","journal-title":"Radiat Oncol"},{"issue":"12","key":"2023111709555286100_ocad180-B41","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1093\/jamia\/ocz135","article-title":"Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting","volume":"26","author":"Segal","year":"2019","journal-title":"J Am Med Inform Assoc"},{"issue":"7","key":"2023111709555286100_ocad180-B42","doi-asserted-by":"crossref","first-page":"e15182","DOI":"10.2196\/15182","article-title":"Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study","volume":"8","author":"Sendak","year":"2020","journal-title":"JMIR Med Inform"},{"issue":"1","key":"2023111709555286100_ocad180-B43","doi-asserted-by":"crossref","first-page":"e000234","DOI":"10.1136\/bmjresp-2017-000234","article-title":"Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial","volume":"4","author":"Shimabukuro","year":"2017","journal-title":"BMJ Open Respir Res"},{"key":"2023111709555286100_ocad180-B44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.phro.2020.12.004","article-title":"Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region","volume":"17","author":"Sibolt","year":"2021","journal-title":"Phys Imaging Radiat Oncol"},{"issue":"2","key":"2023111709555286100_ocad180-B45","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1067\/j.cpradiol.2020.07.006","article-title":"Implementation of an artificial intelligence-based double read system in capturing pulmonary nodule discrepancy in CT studies","volume":"50","author":"Tan","year":"2021","journal-title":"Curr Probl Diagn Radiol"},{"issue":"10","key":"2023111709555286100_ocad180-B46","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1136\/gutjnl-2018-317500","article-title":"Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"issue":"4","key":"2023111709555286100_ocad180-B47","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1053\/j.gastro.2020.06.023","article-title":"Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study","volume":"159","author":"Wang","year":"2020","journal-title":"Gastroenterology"},{"issue":"1","key":"2023111709555286100_ocad180-B48","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1038\/s41598-021-81205-8","article-title":"Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques","volume":"11","author":"Marcos-Pasero","year":"2021","journal-title":"Sci Rep"},{"issue":"10","key":"2023111709555286100_ocad180-B49","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1007\/s00467-018-4015-2","article-title":"Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis","volume":"33","author":"Niel","year":"2018","journal-title":"Pediatr Nephrol"},{"issue":"4","key":"2023111709555286100_ocad180-B50","doi-asserted-by":"crossref","first-page":"e16848","DOI":"10.2196\/16848","article-title":"Development, implementation, and evaluation of a personalized machine learning algorithm for clinical decision support: case study with shingles vaccination","volume":"22","author":"Chen","year":"2020","journal-title":"J Med Internet Res"},{"issue":"5","key":"2023111709555286100_ocad180-B51","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1007\/s12350-017-0823-1","article-title":"Arti\ufb01cial neural network-based model enhances risk strati\ufb01cation and reduces non-invasive cardiac stress imaging compared to Diamond\u2013Forrester and Morise risk assessment models: a prospective study","volume":"25","author":"Isma'eel","year":"2018","journal-title":"J Nucl Cardiol"},{"issue":"6","key":"2023111709555286100_ocad180-B52","doi-asserted-by":"crossref","first-page":"e0234334","DOI":"10.1371\/journal.pone.0234334","article-title":"Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis","volume":"15","author":"Kim","year":"2020","journal-title":"PLoS One"},{"key":"2023111709555286100_ocad180-B53","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.eclinm.2019.03.001","article-title":"Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial","volume":"9","author":"Lin","year":"2019","journal-title":"EClinicalMedicine"},{"issue":"12","key":"2023111709555286100_ocad180-B54","doi-asserted-by":"crossref","first-page":"6671","DOI":"10.1007\/s00464-020-08169-0","article-title":"Applying the electronic nose for pre-operative SARS-CoV-2 screening","volume":"35","author":"Wintjens","year":"2021","journal-title":"Surg Endosc"},{"issue":"2","key":"2023111709555286100_ocad180-B55","doi-asserted-by":"crossref","first-page":"e88","DOI":"10.1016\/S2589-7500(20)30288-0","article-title":"Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study","volume":"3","author":"Xiao","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"5","key":"2023111709555286100_ocad180-B56","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1038\/s41591-021-01335-4","article-title":"Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial","volume":"27","author":"Yao","year":"2021","journal-title":"Nat Med"},{"issue":"2","key":"2023111709555286100_ocad180-B57","doi-asserted-by":"crossref","first-page":"e006286","DOI":"10.1161\/CIRCOUTCOMES.119.006286","article-title":"Implementation of a fast healthcare interoperability resources-based clinical decision support tool for calculating CHA(2)DS(2)-VASc scores","volume":"13","author":"Abedin","year":"2020","journal-title":"Circ Cardiovasc Qual Outcomes"},{"issue":"11","key":"2023111709555286100_ocad180-B58","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1093\/jamia\/ocab123","article-title":"Automation in nursing decision support systems: a systematic review of effects on decision making, care delivery, and patient outcomes","volume":"28","author":"Akbar","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"2023111709555286100_ocad180-B59","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1136\/bmjqs-2019-009857","article-title":"Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study","volume":"29","author":"Carayon","year":"2020","journal-title":"BMJ Qual Saf"},{"issue":"3","key":"2023111709555286100_ocad180-B60","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1111\/jep.13541","article-title":"From clinical decision support to clinical reasoning support systems","volume":"27","author":"van Baalen","year":"2021","journal-title":"J Eval Clin Pract"},{"issue":"1","key":"2023111709555286100_ocad180-B61","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s12911-017-0425-5","article-title":"Automation bias in electronic prescribing","volume":"17","author":"Lyell","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"2023111709555286100_ocad180-B62","doi-asserted-by":"crossref","first-page":"e017833","DOI":"10.1136\/bmjopen-2017-017833","article-title":"Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU","volume":"8","author":"Mao","year":"2018","journal-title":"BMJ Open"},{"key":"2023111709555286100_ocad180-B63","doi-asserted-by":"crossref","first-page":"e070904","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","year":"2022","journal-title":"BMJ"},{"issue":"9","key":"2023111709555286100_ocad180-B64","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"26","author":"Cruz Rivera","year":"2020","journal-title":"Nat Med"},{"issue":"9","key":"2023111709555286100_ocad180-B65","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat Med"},{"issue":"1","key":"2023111709555286100_ocad180-B66","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s12992-020-00584-1","article-title":"Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries","volume":"16","author":"Alami","year":"2020","journal-title":"Glob Health"},{"issue":"4","key":"2023111709555286100_ocad180-B67","doi-asserted-by":"crossref","first-page":"e000798","DOI":"10.1136\/bmjgh-2018-000798","article-title":"Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?","volume":"3","author":"Wahl","year":"2018","journal-title":"BMJ Glob Health"},{"key":"2023111709555286100_ocad180-B68","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/RBME.2020.3017868","article-title":"The promise of clinical decision support systems targetting low-resource settings","volume":"15","author":"Kiyasseh","year":"2020","journal-title":"IEEE Rev Biomed Eng"},{"issue":"4","key":"2023111709555286100_ocad180-B69","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1093\/jamia\/ocad024","article-title":"The Global Health Informatics landscape and JAMIA","volume":"30","author":"Fraser","year":"2023","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2023111709555286100_ocad180-B70","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1038\/s41746-022-00700-y","article-title":"Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review","volume":"5","author":"Ciecierski-Holmes","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"6","key":"2023111709555286100_ocad180-B71","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2021-005190","article-title":"Unravelling \u2018low-resource settings\u2019: a systematic scoping review with qualitative content analysis","volume":"6","author":"van Zyl","year":"2021","journal-title":"BMJ Glob Health"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/30\/12\/2050\/53477676\/ocad180.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/30\/12\/2050\/53477676\/ocad180.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T13:29:44Z","timestamp":1700227784000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/30\/12\/2050\/7255954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,30]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,8,30]]},"published-print":{"date-parts":[[2023,11,17]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocad180","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,12,1]]},"published":{"date-parts":[[2023,8,30]]}}}