{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:27:30Z","timestamp":1758266850092,"version":"3.37.3"},"reference-count":81,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000133","name":"Agency for Healthcare Research and Quality","doi-asserted-by":"publisher","award":["R01HS029324"],"award-info":[{"award-number":["R01HS029324"]}],"id":[{"id":"10.13039\/100000133","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32 EB007507"],"award-info":[{"award-number":["T32 EB007507"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and methods<\/jats:title>\n                  <jats:p>A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients\u2019 experiences and attitudes via focus groups and interviews.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>For Stage 1, a total of 281 respondents\u2019 (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR\u2009=\u20092.97; 95% CI, 1.36-6.49) and embrace (OR\u2009=\u20092.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS\u2019s role in their care to be disseminated by surgeons across multiple platforms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion and conclusion<\/jats:title>\n                  <jats:p>The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS\u2019s role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae257","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T17:40:13Z","timestamp":1728927613000},"page":"150-162","source":"Crossref","is-referenced-by-count":3,"title":["Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians\u2019 use of machine learning in perioperative care"],"prefix":"10.1093","volume":"32","author":[{"given":"Xiomara T","family":"Gonzalez","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at Austin , Austin, TX 78712,","place":["United States"]}]},{"given":"Karen","family":"Steger-May","sequence":"additional","affiliation":[{"name":"Center for Biostatistics and Data Science, Washington University School of Medicine , St Louis, MO 63110,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0235-1632","authenticated-orcid":false,"given":"Joanna","family":"Abraham","sequence":"additional","affiliation":[{"name":"Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine , St Louis, MO 63110,","place":["United States"]},{"name":"Department of Anesthesiology, Washington University School of Medicine, Washington University in St Louis , St Louis, MO 63110,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"2024121616110493000_ocae257-B1","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1016\/S0140-6736(15)60806-6","article-title":"Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes","volume":"385 Suppl 2","author":"Weiser","year":"2015","journal-title":"Lancet"},{"year":"2015","author":"National Quality Forum","key":"2024121616110493000_ocae257-B2"},{"key":"2024121616110493000_ocae257-B3","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1186\/s12893-021-01392-z","article-title":"Post-operative complications: an observational study of trends in the United States from 2012 to 2018","volume":"21","author":"Dencker","year":"2021","journal-title":"BMC Surg"},{"key":"2024121616110493000_ocae257-B4","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1097\/SLA.0000000000001390","article-title":"Implications of multiple complications on the post-operative recovery of general surgery patients","volume":"263","author":"Tevis","year":"2016","journal-title":"Ann Surg"},{"key":"2024121616110493000_ocae257-B5","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/j.surg.2023.04.048","article-title":"Preoperative risk factors and post-operative complications associated with mortality after outpatient surgery in a broad surgical population: an analysis of 2.8 million ACS-NSQIP patients","volume":"174","author":"Alder","year":"2023","journal-title":"Surgery"},{"key":"2024121616110493000_ocae257-B6","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1097\/TA.0000000000000611","article-title":"Impact of specific post-operative complications on the outcomes of emergency general surgery patients","volume":"78","author":"McCoy","year":"2015","journal-title":"J Trauma Acute Care Surg"},{"key":"2024121616110493000_ocae257-B7","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1503\/cjs.027810","article-title":"Variable impact of complications in general surgery: a prospective cohort study","volume":"55","author":"Bosma","year":"2012","journal-title":"Can J Surg"},{"key":"2024121616110493000_ocae257-B8","doi-asserted-by":"publisher","first-page":"e230080","DOI":"10.57264\/cer-2023-0080","article-title":"Impact of surgical complications on hospital costs and revenues: retrospective database study of Medicare claims","volume":"12","author":"Haidar","year":"2023","journal-title":"J Comp Eff Res"},{"key":"2024121616110493000_ocae257-B9","doi-asserted-by":"publisher","first-page":"e375","DOI":"10.1097\/SLA.0000000000004243","article-title":"Hospital costs following surgical complications: a value-driven outcomes analysis of cost savings due to complication prevention","volume":"275","author":"Stokes","year":"2022","journal-title":"Ann Surg"},{"key":"2024121616110493000_ocae257-B10","doi-asserted-by":"publisher","first-page":"e007224","DOI":"10.1136\/bmjopen-2014-007224","article-title":"Surgical complications and their impact on patients\u2019 psychosocial well-being: a systematic review and meta-analysis","volume":"6","author":"Pinto","year":"2016","journal-title":"BMJ Open"},{"key":"2024121616110493000_ocae257-B11","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1002\/nop2.620","article-title":"Symptoms of anxiety and depression in surgical patients at the hospital, 6 weeks and 6 months postsurgery: a questionnaire study","volume":"8","author":"Sveinsd\u00f3ttir","year":"2020","journal-title":"Nurs Open"},{"key":"2024121616110493000_ocae257-B12","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.amjsurg.2012.11.009","article-title":"Surgical adverse events: a systematic review","volume":"206","author":"Anderson","year":"2013","journal-title":"Am J Surg"},{"key":"2024121616110493000_ocae257-B13","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1001\/archsurg.137.5.611","article-title":"Complications in surgical patients","volume":"137","author":"Healey","year":"2002","journal-title":"Arch Surg"},{"key":"2024121616110493000_ocae257-B14","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/s00268-020-05858-8","article-title":"Preventable morbidity and mortality among non-trauma emergency surgery patients: the role of personal performance and system flaws in adverse events","volume":"45","author":"Velmahos","year":"2021","journal-title":"World J Surg"},{"key":"2024121616110493000_ocae257-B15","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1097\/SLA.0000000000002693","article-title":"Artificial intelligence in surgery: promises and perils","volume":"268","author":"Hashimoto","year":"2018","journal-title":"Ann Surg"},{"key":"2024121616110493000_ocae257-B16","doi-asserted-by":"publisher","first-page":"872675","DOI":"10.3389\/fdgth.2022.872675","article-title":"Artificial intelligence in perioperative medicine: a proposed common language with applications to FDA-approved devices","volume":"4","author":"Melvin","year":"2022","journal-title":"Front Digit Health"},{"key":"2024121616110493000_ocae257-B17","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s10916-018-0977-7","article-title":"A system for automated determination of perioperative patient acuity","volume":"42","author":"Zhang","year":"2018","journal-title":"J Med Syst"},{"key":"2024121616110493000_ocae257-B18","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1097\/SLA.0000000000002706","article-title":"MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery","volume":"269","author":"Bihorac","year":"2019","journal-title":"Ann Surg"},{"key":"2024121616110493000_ocae257-B19","doi-asserted-by":"publisher","first-page":"e212240","DOI":"10.1001\/jamanetworkopen.2021.2240","article-title":"Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of post-operative complications","volume":"4","author":"Xue","year":"2021","journal-title":"JAMA Netw Open"},{"key":"2024121616110493000_ocae257-B20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1001\/jamasurg.2019.4917","article-title":"Artificial intelligence and surgical decision-making","volume":"155","author":"Loftus","year":"2020","journal-title":"JAMA Surg"},{"key":"2024121616110493000_ocae257-B21","doi-asserted-by":"publisher","first-page":"2624","DOI":"10.1016\/j.arth.2019.06.007","article-title":"Clinical and statistical validation of a probabilistic prediction tool of total knee arthroplasty outcome","volume":"34","author":"Twiggs","year":"2019","journal-title":"J Arthroplasty"},{"key":"2024121616110493000_ocae257-B22","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1177\/1460458218813602","article-title":"Machine learning for identification of surgeries with high risks of cancellation","volume":"26","author":"Luo","year":"2020","journal-title":"Health Informatics J"},{"key":"2024121616110493000_ocae257-B23","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s10916-018-1151-y","article-title":"A machine learning approach to predicting case duration for robot-assisted surgery","volume":"43","author":"Zhao","year":"2019","journal-title":"J Med Syst"},{"key":"2024121616110493000_ocae257-B24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s10916-019-1512-1","article-title":"Artificial intelligence: a new tool in operating room management. role of machine learning models in operating room optimization","volume":"44","author":"Bellini","year":"2019","journal-title":"J Med Syst"},{"key":"2024121616110493000_ocae257-B25","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1001\/jamasurg.2019.2979","article-title":"Novel machine learning approach to identify preoperative risk factors associated with super-utilization of medicare expenditure following surgery","volume":"154","author":"Hyer","year":"2019","journal-title":"JAMA Surg"},{"key":"2024121616110493000_ocae257-B26","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TMI.2017.2739110","article-title":"Automatic localization of the needle target for ultrasound-guided epidural injections","volume":"37","author":"Pesteie","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"2024121616110493000_ocae257-B27","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/s10877-022-00906-1","article-title":"Reduced post-operative pain in patients receiving nociception monitor guided analgesia during elective major abdominal surgery: a randomized, controlled trial","volume":"37","author":"Fuica","year":"2023","journal-title":"J Clin Monit Comput"},{"key":"2024121616110493000_ocae257-B28","doi-asserted-by":"publisher","first-page":"5884","DOI":"10.3390\/jcm10245884","article-title":"The use of the hypotension prediction index integrated in an algorithm of goal directed hemodynamic treatment during moderate and high-risk surgery","volume":"10","author":"Tsoumpa","year":"2021","journal-title":"J Clin Med"},{"key":"2024121616110493000_ocae257-B29","doi-asserted-by":"publisher","first-page":"e0231172","DOI":"10.1371\/journal.pone.0231172","article-title":"Development of a prediction model for hypotension after induction of anesthesia using machine learning","volume":"15","author":"Kang","year":"2020","journal-title":"PLoS One"},{"key":"2024121616110493000_ocae257-B30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1213\/ANE.0000000000003798","article-title":"A predictive model for determining patients not requiring prolonged hospital length of stay after elective primary total hip arthroplasty","volume":"129","author":"Gabriel","year":"2019","journal-title":"Anesth Analg"},{"key":"2024121616110493000_ocae257-B31","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/j.jtcvs.2020.09.076","article-title":"Unraveling the impact of time-dependent perioperative variables on 30-day readmission after coronary artery bypass surgery","volume":"164","author":"Manyam","year":"2022","journal-title":"J Thorac Cardiovasc Surg"},{"key":"2024121616110493000_ocae257-B32","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1038\/s41746-020-00377-1","article-title":"Development and validation of an interpretable neural network for prediction of post-operative in-hospital mortality","volume":"4","author":"Lee","year":"2021","journal-title":"NPJ Digit Med"},{"key":"2024121616110493000_ocae257-B33","doi-asserted-by":"publisher","first-page":"e0206862","DOI":"10.1371\/journal.pone.0206862","article-title":"Optimal intensive care outcome prediction over time using machine learning","volume":"13","author":"Meiring","year":"2018","journal-title":"PLoS One"},{"key":"2024121616110493000_ocae257-B34","doi-asserted-by":"publisher","first-page":"4455","DOI":"10.3390\/s23094455","article-title":"Machine learning for post-operative continuous recovery scores of oncology patients in perioperative care with data from wearables","volume":"23","author":"van den Eijnden","year":"2023","journal-title":"Sensors (Basel)"},{"key":"2024121616110493000_ocae257-B35","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.surg.2022.03.031","article-title":"Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study","volume":"172","author":"van de Sande","year":"2022","journal-title":"Surgery"},{"key":"2024121616110493000_ocae257-B36","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/s21010002","article-title":"Individualised responsible artificial intelligence for home-based rehabilitation","volume":"21","author":"Vourganas","year":"2021","journal-title":"Sensors (Basel)"},{"key":"2024121616110493000_ocae257-B37","doi-asserted-by":"publisher","first-page":"104270","DOI":"10.1016\/j.jbi.2022.104270","article-title":"Integrating machine learning predictions for perioperative risk management: towards an empirical design of a flexible-standardized risk assessment tool","volume":"137","author":"Abraham","year":"2023","journal-title":"J Biomed Inform"},{"key":"2024121616110493000_ocae257-B38","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1055\/s-0040-1721780","article-title":"Ascertaining design requirements for post-operative care transition interventions","volume":"12","author":"Abraham","year":"2021","journal-title":"Appl Clin Inform"},{"key":"2024121616110493000_ocae257-B39","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1213\/ANE.0000000000006577","article-title":"User-centered design of a machine learning dashboard for prediction of post-operative complications","volume":"138","author":"Fritz","year":"2023","journal-title":"Anesth Analg"},{"key":"2024121616110493000_ocae257-B40","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1001\/jamasurg.2020.6361","article-title":"Effect of a predictive model on planned surgical duration accuracy, patient wait time, and use of presurgical resources: a randomized clinical trial","volume":"156","author":"Str\u00f6mblad","year":"2021","journal-title":"JAMA Surg"},{"key":"2024121616110493000_ocae257-B41","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1097\/ALN.0000000000003557","article-title":"Hypotension prediction index for prevention of hypotension during moderate- to high-risk noncardiac surgery","volume":"133","author":"Maheshwari","year":"2020","journal-title":"Anesthesiology"},{"key":"2024121616110493000_ocae257-B42","doi-asserted-by":"publisher","first-page":"392","DOI":"10.3390\/jcm11020392","article-title":"Proactive management of intraoperative hypotension reduces biomarkers of organ injury and oxidative stress during elective non-cardiac surgery: a pilot randomized controlled trial","volume":"11","author":"Murabito","year":"2022","journal-title":"J Clin Med"},{"key":"2024121616110493000_ocae257-B43","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1016\/j.bja.2020.07.057","article-title":"Reduced post-operative pain using Nociception Level-guided fentanyl dosing during sevoflurane anaesthesia: a randomised controlled trial","volume":"125","author":"Meijer","year":"2020","journal-title":"Br J Anaesth"},{"key":"2024121616110493000_ocae257-B44","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1213\/ANE.0000000000006351","article-title":"Nociception level index-guided intraoperative analgesia for improved post-operative recovery: a randomized trial","volume":"136","author":"Ruetzler","year":"2023","journal-title":"Anesth Analg"},{"year":"2023","author":"Pew Research Center","key":"2024121616110493000_ocae257-B45"},{"key":"2024121616110493000_ocae257-B46","doi-asserted-by":"publisher","first-page":"e627","DOI":"10.1016\/j.wneu.2020.03.029","article-title":"Attitudes of patients and their relatives toward artificial intelligence in neurosurgery","volume":"138","author":"Palmisciano","year":"2020","journal-title":"World Neurosurg"},{"key":"2024121616110493000_ocae257-B47","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1525822X05282260","article-title":"Using mixed-methods sequential explanatory design: from theory to practice","volume":"18","author":"Ivankova","year":"2006","journal-title":"Field Methods"},{"key":"2024121616110493000_ocae257-B48","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1001\/jama.1965.03080030024005","article-title":"Major and minor surgery","volume":"191","author":"Small","year":"1965","journal-title":"JAMA"},{"key":"2024121616110493000_ocae257-B49","doi-asserted-by":"publisher","first-page":"e599","DOI":"10.1016\/S2589-7500(21)00132-1","article-title":"Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review","volume":"3","author":"Young","year":"2021","journal-title":"Lancet Digit Health"},{"key":"2024121616110493000_ocae257-B50","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1186\/s12911-021-01586-8","article-title":"Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey","volume":"21","author":"Antes","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"2024121616110493000_ocae257-B51","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s12911-020-01191-1","article-title":"Use of AI-based tools for healthcare purposes: a survey study from consumers\u2019 perspectives","volume":"20","author":"Esmaeilzadeh","year":"2020","journal-title":"BMC Med Inform Decis Mak"},{"year":"2023","author":"Elwy","key":"2024121616110493000_ocae257-B52"},{"key":"2024121616110493000_ocae257-B53","doi-asserted-by":"publisher","first-page":"2156","DOI":"10.1016\/j.chb.2013.05.009","article-title":"Separate but equal? A comparison of participants and data gathered via Amazon\u2019s MTurk, social media, and face-to-face behavioral testing","volume":"29","author":"Casler","year":"2013","journal-title":"Comput Hum Behav"},{"key":"2024121616110493000_ocae257-B54","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: a roadmap for responsible machine learning for health care","volume":"25","author":"Wiens","year":"2019","journal-title":"Nat Med"},{"key":"2024121616110493000_ocae257-B55","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1177\/1525822X05279903","article-title":"How many interviews are enough?: an experiment with data saturation and variability","volume":"18","author":"Guest","year":"2006","journal-title":"Field Methods"},{"key":"2024121616110493000_ocae257-B56","doi-asserted-by":"publisher","first-page":"114523","DOI":"10.1016\/j.socscimed.2021.114523","article-title":"Sample sizes for saturation in qualitative research: a systematic review of empirical tests","volume":"292","author":"Hennink","year":"2022","journal-title":"Soc Sci Med"},{"key":"2024121616110493000_ocae257-B57","doi-asserted-by":"publisher","first-page":"178","DOI":"10.2307\/2290467","article-title":"Generalized collinearity diagnostics","volume":"87","author":"Fox","year":"1992","journal-title":"J Am Stat Assoc"},{"year":"2013","key":"2024121616110493000_ocae257-B58"},{"key":"2024121616110493000_ocae257-B59","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1191\/1478088706qp063oa","article-title":"Using thematic analysis in psychology","volume":"3","author":"Braun","year":"2006","journal-title":"Qual Res Psychol"},{"volume-title":"Successful Qualitative Research: A Practical Guide for Beginners","year":"2013","author":"Braun","key":"2024121616110493000_ocae257-B60"},{"key":"2024121616110493000_ocae257-B61","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1097\/DCC.0000000000000253","article-title":"Rigor or reliability and validity in qualitative research: perspectives, strategies, reconceptualization, and recommendations","volume":"36","author":"Cypress","year":"2017","journal-title":"Dimens Crit Care Nurs"},{"key":"2024121616110493000_ocae257-B62","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1159\/000536393","article-title":"Artificial intelligence in surgery: the future is now","volume":"65","author":"Guni","year":"2024","journal-title":"Eur Surg Res"},{"key":"2024121616110493000_ocae257-B63","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s11701-019-00960-z","article-title":"Gender differences in understanding and acceptance of robot-assisted surgery","volume":"14","author":"McDermott","year":"2020","journal-title":"J Robot Surg"},{"key":"2024121616110493000_ocae257-B64","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s43465-023-00845-2","article-title":"Patient perspectives on artificial intelligence in healthcare decision making: a multi-center comparative study","volume":"57","author":"Parry","year":"2023","journal-title":"Indian J Orthop"},{"key":"2024121616110493000_ocae257-B65","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s12910-022-00842-4","article-title":"I don\u2019t think people are ready to trust these algorithms at face value\u201d: trust and the use of machine learning algorithms in the diagnosis of rare disease","volume":"23","author":"Hallowell","year":"2022","journal-title":"BMC Med Ethics"},{"year":"2023","author":"Faverio","key":"2024121616110493000_ocae257-B66"},{"key":"2024121616110493000_ocae257-B67","doi-asserted-by":"publisher","DOI":"10.1007\/s44174-023-00063-2","article-title":"Drawbacks of artificial intelligence and their potential solutions in the healthcare sector","author":"Khan","journal-title":"Biomed Mater Devices"},{"key":"2024121616110493000_ocae257-B68","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1001\/jamadermatol.2019.5014","article-title":"Patient perspectives on the use of artificial intelligence for skin cancer screening: a qualitative study","volume":"156","author":"Nelson","year":"2020","journal-title":"JAMA Dermatol"},{"key":"2024121616110493000_ocae257-B69","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1089\/end.2020.0137","article-title":"Public perceptions of artificial intelligence and robotics in medicine","volume":"34","author":"Stai","year":"2020","journal-title":"J Endourol"},{"key":"2024121616110493000_ocae257-B70","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1016\/j.jacr.2018.12.043","article-title":"A qualitative study to understand patient perspective on the use of artificial intelligence in radiology","volume":"16","author":"Haan","year":"2019","journal-title":"J Am Coll Radiol"},{"key":"2024121616110493000_ocae257-B71","doi-asserted-by":"publisher","first-page":"e100233","DOI":"10.1136\/bmjhci-2020-100233","article-title":"Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography","volume":"27","author":"York","year":"2020","journal-title":"BMJ Health Care Inform"},{"key":"2024121616110493000_ocae257-B72","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s13012-015-0209-1","article-title":"A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project","volume":"10","author":"Powell","year":"2015","journal-title":"Implement Sci"},{"key":"2024121616110493000_ocae257-B73","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1007\/s00146-022-01617-6","article-title":"Connecting ethics and epistemology of AI","volume":"39","author":"Russo","year":"2023","journal-title":"AI Soc"},{"author":"Ehsan","key":"2024121616110493000_ocae257-B74"},{"key":"2024121616110493000_ocae257-B75","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","article-title":"Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond","volume":"77","author":"Yang","year":"2022","journal-title":"Inf Fusion"},{"key":"2024121616110493000_ocae257-B76","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1038\/s41746-023-00753-7","article-title":"Making machine learning matter to clinicians: model actionability in medical decision-making","volume":"6","author":"Ehrmann","year":"2023","journal-title":"NPJ Digit Med"},{"key":"2024121616110493000_ocae257-B77","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1001\/jama.2019.10306","article-title":"Making machine learning models clinically useful","volume":"322","author":"Shah","year":"2019","journal-title":"JAMA"},{"key":"2024121616110493000_ocae257-B78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0155-4","article-title":"The \u201cinconvenient truth\u201d about AI in healthcare","volume":"2","author":"Panch","year":"2019","journal-title":"NPJ Digit Med"},{"key":"2024121616110493000_ocae257-B79","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-981-15-0947-6_31","volume-title":"Embedded Systems and Artificial Intelligence","author":"Adadi","year":"2020"},{"year":"2023","author":"Tyson","key":"2024121616110493000_ocae257-B80"},{"key":"2024121616110493000_ocae257-B81","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1002\/bdm.1753","article-title":"Data collection in a flat world: the strengths and weaknesses of mechanical Turk samples","volume":"26","author":"Goodman","year":"2013","journal-title":"Behav Decision Mak"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/32\/1\/150\/61201916\/ocae257.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/32\/1\/150\/61201916\/ocae257.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T16:11:17Z","timestamp":1734365477000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/32\/1\/150\/7821242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,14]]},"references-count":81,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,10,14]]},"published-print":{"date-parts":[[2025,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocae257","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"type":"print","value":"1067-5027"},{"type":"electronic","value":"1527-974X"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,10,14]]}}}