{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:52:46Z","timestamp":1781679166654,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100015688","name":"\u00d3buda University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100015688","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist\u2019s and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents\u2019 electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD\u2009=\u200911.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (<jats:italic>p<\/jats:italic>\u2009=\u20090.677), and a significantly higher average amount for radiologist vs. AI in the second task (<jats:italic>p<\/jats:italic>\u2009=\u20090.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-024-02470-x","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T15:01:46Z","timestamp":1711724506000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8131-2839","authenticated-orcid":false,"given":"\u00c1ron","family":"H\u00f6lgyesi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1992-6087","authenticated-orcid":false,"given":"Zsombor","family":"Zrubka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9285-8746","authenticated-orcid":false,"given":"L\u00e1szl\u00f3","family":"Gul\u00e1csi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2899-8557","authenticated-orcid":false,"given":"Petra","family":"Baji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1402-1139","authenticated-orcid":false,"given":"Tam\u00e1s","family":"Haidegger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8096-9628","authenticated-orcid":false,"given":"Mikl\u00f3s","family":"Kozlovszky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8000-4367","authenticated-orcid":false,"given":"Mikl\u00f3s","family":"Weszl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-0800","authenticated-orcid":false,"given":"Levente","family":"Kov\u00e1cs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9636-6012","authenticated-orcid":false,"given":"M\u00e1rta","family":"P\u00e9ntek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"2470_CR1","doi-asserted-by":"crossref","unstructured":"Akhtar N, Khan N, Qayyum S, Qureshi MI, Hishan SS. Efficacy and pitfalls of digital technologies in healthcare services: a systematic review of two decades. Front Public Health. 2022;10.","DOI":"10.3389\/fpubh.2022.869793"},{"issue":"7","key":"2470_CR2","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/JPROC.2022.3166253","volume":"110","author":"G Fichtinger","year":"2022","unstructured":"Fichtinger G, Troccaz J, Haidegger T. Image-guided interventional robotics: lost in translation? Proc IEEE. 2022;110(7):932\u201350.","journal-title":"Proc IEEE"},{"key":"2470_CR3","doi-asserted-by":"crossref","unstructured":"Haidegger T, Speidel S, Stoyanov D, Satava RM. Robot-assisted minimally invasive surgery\u2014Surgical robotics in the data age. Proceedings of the IEEE. 2022;110(7):835\u2013\u200946.","DOI":"10.1109\/JPROC.2022.3180350"},{"issue":"2","key":"2470_CR4","doi-asserted-by":"publisher","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","volume":"6","author":"T Davenport","year":"2019","unstructured":"Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94\u20138.","journal-title":"Future Healthc J"},{"issue":"5","key":"2470_CR5","doi-asserted-by":"publisher","first-page":"13","DOI":"10.12700\/APH.18.5.2021.5.3","volume":"18","author":"A Khamis","year":"2021","unstructured":"Khamis A, Meng J, Wang J, Azar AT, Prestes E, Tak\u00e1cs \u00c1, et al. Robotics and intelligent systems against a pandemic. Acta Polytech Hungarica. 2021;18(5):13\u201335.","journal-title":"Acta Polytech Hungarica"},{"issue":"1","key":"2470_CR6","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","volume":"2","author":"F Pesapane","year":"2018","unstructured":"Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Experimental. 2018;2(1):35.","journal-title":"Eur Radiol Experimental"},{"issue":"9","key":"2470_CR7","doi-asserted-by":"publisher","first-page":"e486","DOI":"10.1016\/S2589-7500(20)30160-6","volume":"2","author":"O Oren","year":"2020","unstructured":"Oren O, Gersh BJ, Bhatt DL. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health. 2020;2(9):e486\u2013e8.","journal-title":"Lancet Digit Health"},{"issue":"5","key":"2470_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.12700\/APH.19.5.2022.5.7","volume":"19","author":"SR Chandaran","year":"2022","unstructured":"Chandaran SR, Muthusamy G, Sevalaiappan LR, Senthilkumaran N. Deep learning-based transfer learning model in diagnosis of diseases with brain magnetic resonance imaging. Acta Polytech Hungarica. 2022;19(5):127\u201347.","journal-title":"Acta Polytech Hungarica"},{"issue":"9 Pt B","key":"2470_CR9","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1016\/j.jacr.2019.05.047","volume":"16","author":"T Mart\u00edn Noguerol","year":"2019","unstructured":"Mart\u00edn Noguerol T, Paulano-Godino F, Mart\u00edn-Valdivia MT, Menias CO, Luna A, Strengths. Weaknesses, opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology. J Am Coll Radiol. 2019;16(9 Pt B):1239\u201347.","journal-title":"J Am Coll Radiol"},{"key":"2470_CR10","doi-asserted-by":"publisher","first-page":"e113","DOI":"10.5114\/pjr.2022.113531","volume":"87","author":"J Waller","year":"2022","unstructured":"Waller J, O\u2019Connor A, Rafaat E, Amireh A, Dempsey J, Martin C, et al. Applications and challenges of artificial intelligence in diagnostic and interventional radiology. Pol J Radiol. 2022;87:e113\u2013e7.","journal-title":"Pol J Radiol"},{"issue":"11","key":"2470_CR11","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1111\/ijcp.12492","volume":"68","author":"A Hussain","year":"2014","unstructured":"Hussain A, Malik A, Halim MU, Ali AM. The use of robotics in surgery: a review. Int J Clin Pract. 2014;68(11):1376\u201382.","journal-title":"Int J Clin Pract"},{"issue":"1171","key":"2470_CR12","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1136\/postgradmedj-2021-141135","volume":"99","author":"V Kumar","year":"2023","unstructured":"Kumar V, Patel S, Baburaj V, Rajnish RK, Aggarwal S. Does robotic-assisted surgery improve outcomes of total hip arthroplasty compared to manual technique? A systematic review and meta-analysis. Postgrad Med J. 2023;99(1171):375\u201383.","journal-title":"Postgrad Med J"},{"issue":"1112","key":"2470_CR13","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1136\/postgradmedj-2017-135352","volume":"94","author":"X Chen","year":"2018","unstructured":"Chen X, Xiong J, Wang P, Zhu S, Qi W, Peng H, et al. Robotic-assisted compared with conventional total hip arthroplasty: systematic review and meta-analysis. Postgrad Med J. 2018;94(1112):335\u201341.","journal-title":"Postgrad Med J"},{"issue":"4","key":"2470_CR14","doi-asserted-by":"publisher","first-page":"e2113","DOI":"10.1002\/rcs.2113","volume":"16","author":"IJY Wee","year":"2020","unstructured":"Wee IJY, Kuo LJ, Ngu JC. A systematic review of the true benefit of robotic surgery: Ergonomics. Int J Med Robot. 2020;16(4):e2113.","journal-title":"Int J Med Robot"},{"issue":"6","key":"2470_CR15","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s00264-018-4140-3","volume":"43","author":"S Karunaratne","year":"2019","unstructured":"Karunaratne S, Duan M, Pappas E, Fritsch B, Boyle R, Gupta S, et al. The effectiveness of robotic hip and knee arthroplasty on patient-reported outcomes: a systematic review and meta-analysis. Int Orthop. 2019;43(6):1283\u201395.","journal-title":"Int Orthop"},{"issue":"6","key":"2470_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1302\/0301-620X.103B6.BJJ-2020-1856.R1","volume":"103\u2013B","author":"N Ng","year":"2021","unstructured":"Ng N, Gaston P, Simpson PM, Macpherson GJ, Patton JT, Clement ND. Robotic arm-assisted versus manual total hip arthroplasty. Bone Joint J. 2021;103\u2013B(6):1009\u201320.","journal-title":"Bone Joint J"},{"key":"2470_CR17","doi-asserted-by":"publisher","first-page":"990604","DOI":"10.3389\/fmed.2022.990604","volume":"9","author":"M Chen","year":"2022","unstructured":"Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, et al. Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey. Front Med. 2022;9:990604.","journal-title":"Front Med"},{"issue":"1","key":"2470_CR18","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/s41746-023-00852-5","volume":"6","author":"SI Lambert","year":"2023","unstructured":"Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello C-P, et al. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. Npj Digit Med. 2023;6(1):111.","journal-title":"Npj Digit Med"},{"issue":"1","key":"2470_CR19","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1177\/02666669211025076","volume":"39","author":"S Aljarboa","year":"2021","unstructured":"Aljarboa S, Miah SJ. Acceptance of clinical decision support systems in Saudi healthcare organisations. Inform Dev. 2021;39(1):86\u2013106.","journal-title":"Inform Dev"},{"issue":"9","key":"2470_CR20","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1016\/j.arth.2023.03.020","volume":"38","author":"MS Abdelaal","year":"2023","unstructured":"Abdelaal MS, Wiafe BM, Khan IA, Magnuson JA, Saxena A, Smith EB, et al. Robotic-assisted total knee arthroplasty: what are patients\u2019 perspectives, understanding and expectations? J Arthroplast. 2023;38(9):1726\u201333e4.","journal-title":"J Arthroplast"},{"issue":"8","key":"2470_CR21","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1016\/j.jacr.2020.01.007","volume":"17","author":"SJ Adams","year":"2020","unstructured":"Adams SJ, Tang R, Babyn P. Patient perspectives and priorities regarding Artificial Intelligence in Radiology: opportunities for patient-centered Radiology. J Am Coll Radiol. 2020;17(8):1034\u20136.","journal-title":"J Am Coll Radiol"},{"key":"2470_CR22","doi-asserted-by":"crossref","first-page":"205520761987180","DOI":"10.1177\/2055207619871808","volume":"5","author":"T Nadarzynski","year":"2019","unstructured":"Nadarzynski T, Miles O, Cowie A, Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study. Digit Health. 2019;5:2055207619871808.","journal-title":"Digit Health"},{"issue":"10","key":"2470_CR23","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1016\/j.jacr.2018.12.043","volume":"16","author":"M Haan","year":"2019","unstructured":"Haan M, Ongena YP, Hommes S, Kwee TC, Yakar D. A qualitative study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology. J Am Coll Radiol. 2019;16(10):1416\u20139.","journal-title":"J Am Coll Radiol"},{"issue":"9","key":"2470_CR24","doi-asserted-by":"publisher","first-page":"e599","DOI":"10.1016\/S2589-7500(21)00132-1","volume":"3","author":"AT Young","year":"2021","unstructured":"Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021;3(9):e599\u2013e611.","journal-title":"Lancet Digit Health"},{"issue":"4","key":"2470_CR25","doi-asserted-by":"publisher","first-page":"3407","DOI":"10.3390\/ijerph20043407","volume":"20","author":"AI Stoumpos","year":"2023","unstructured":"Stoumpos AI, Kitsios F, Talias MA. Digital Transformation in Healthcare: Technology Acceptance and its applications. Int J Environ Res Public Health. 2023;20(4):3407.","journal-title":"Int J Environ Res Public Health"},{"issue":"4","key":"2470_CR26","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1002\/(SICI)1099-1050(199806)7:4<313::AID-HEC350>3.0.CO;2-B","volume":"7","author":"A Diener","year":"1998","unstructured":"Diener A, O\u2019Brien B, Gafni A. Health care contingent valuation studies: a review and classification of the literature. Health Econ. 1998;7(4):313\u201326.","journal-title":"Health Econ"},{"issue":"6","key":"2470_CR27","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/S0167-6296(02)00052-8","volume":"21","author":"P Shackley","year":"2002","unstructured":"Shackley P, Donaldson C. Should we use willingness to pay to elicit community preferences for health care? New evidence from using a \u2018marginal\u2019 approach. J Health Econ. 2002;21(6):971\u201391.","journal-title":"J Health Econ"},{"key":"2470_CR28","doi-asserted-by":"crossref","unstructured":"Markandya A, Ortiz RA, Chiabai A. Estimating environmental health costs: General introduction to valuation of human health risks. 2019.","DOI":"10.1016\/B978-0-12-409548-9.10657-8"},{"issue":"1","key":"2470_CR29","doi-asserted-by":"publisher","first-page":"9","DOI":"10.2165\/00019053-199915010-00002","volume":"15","author":"MV Bala","year":"1999","unstructured":"Bala MV, Mauskopf JA, Wood LL. Willingness to pay as a measure of health benefits. PharmacoEconomics. 1999;15(1):9\u201318.","journal-title":"PharmacoEconomics"},{"issue":"1","key":"2470_CR30","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1093\/bmb\/lds020","volume":"103","author":"S Ali","year":"2012","unstructured":"Ali S, Ronaldson S. Ordinal preference elicitation methods in health economics and health services research: using discrete choice experiments and ranking methods. Br Med Bull. 2012;103(1):21\u201344.","journal-title":"Br Med Bull"},{"issue":"5","key":"2470_CR31","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1586\/17434440.2015.1080118","volume":"12","author":"G Wilkinson","year":"2015","unstructured":"Wilkinson G, Drummond M. Alternative approaches for assessing the socioeconomic benefits of medical devices: a systematic review. Expert Rev Med Devices. 2015;12(5):629\u201348.","journal-title":"Expert Rev Med Devices"},{"issue":"4","key":"2470_CR32","doi-asserted-by":"publisher","first-page":"e0284577","DOI":"10.1371\/journal.pone.0284577","volume":"18","author":"\u00c1 H\u00f6lgyesi","year":"2023","unstructured":"H\u00f6lgyesi \u00c1, T\u00f3th B, Kozlovszky M, Kuti J, Weszl M, Bal\u00e1zs G, et al. Epidemiology and patients\u2019 self-reported knowledge of implantable medical devices: results of a cross-sectional survey in Hungary. PLoS ONE. 2023;18(4):e0284577.","journal-title":"PLoS ONE"},{"issue":"4","key":"2470_CR33","doi-asserted-by":"publisher","first-page":"e27","DOI":"10.2196\/jmir.8.4.e27","volume":"8","author":"CD Norman","year":"2006","unstructured":"Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. 2006;8(4):e27.","journal-title":"J Med Internet Res"},{"issue":"Suppl 1","key":"2470_CR34","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10198-019-01062-1","volume":"20","author":"Z Zrubka","year":"2019","unstructured":"Zrubka Z, Hajdu O, Rencz F, Baji P, Gul\u00e1csi L, P\u00e9ntek M. Psychometric properties of the Hungarian version of the eHealth literacy scale. Eur J Health Econ. 2019;20(Suppl 1):57\u201369.","journal-title":"Eur J Health Econ"},{"issue":"10","key":"2470_CR35","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1007\/s11136-011-9903-x","volume":"20","author":"M Herdman","year":"2011","unstructured":"Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual life Research: Int J Qual life Aspects Treat care Rehabilitation. 2011;20(10):1727\u201336.","journal-title":"Qual life Research: Int J Qual life Aspects Treat care Rehabilitation"},{"issue":"9","key":"2470_CR36","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1016\/j.jval.2020.03.019","volume":"23","author":"F Rencz","year":"2020","unstructured":"Rencz F, Brodszky V, Gul\u00e1csi L, Golicki D, Ruzsa G, Pickard AS, et al. Parallel valuation of the EQ-5D-3L and EQ-5D-5L by Time Trade-Off in Hungary. Value Health: J Int Soc Pharmacoeconomics Outcomes Res. 2020;23(9):1235\u201345.","journal-title":"Value Health: J Int Soc Pharmacoeconomics Outcomes Res"},{"issue":"3","key":"2470_CR37","doi-asserted-by":"publisher","first-page":"872","DOI":"10.2307\/2578140","volume":"61","author":"RN Parker","year":"1983","unstructured":"Parker RN, Fenwick R. The pareto curve and its utility for Open-Ended Income distributions in Survey Research. Soc Forces. 1983;61(3):872\u201385.","journal-title":"Soc Forces"},{"key":"2470_CR38","unstructured":"Office HCS. Net and gross income per capita by income deciles (HUF\/person\/year) 2019 [Available from: https:\/\/www.ksh.hu\/stadat_files\/jov\/hu\/jov0005.html."},{"issue":"4","key":"2470_CR39","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1177\/014662168801200410","volume":"12","author":"J Cohen","year":"1988","unstructured":"Cohen J. Set correlation and contingency tables. Appl Psychol Meas. 1988;12(4):425\u201334.","journal-title":"Appl Psychol Meas"},{"issue":"3\u20134","key":"2470_CR40","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","volume":"52","author":"SS Shapiro","year":"1965","unstructured":"Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples)\u2020. Biometrika. 1965;52(3\u20134):591\u2013611.","journal-title":"Biometrika"},{"key":"2470_CR41","doi-asserted-by":"crossref","unstructured":"Cohen J. Statistical power analysis for the behavioral sciences. Academic; 2013.","DOI":"10.4324\/9780203771587"},{"issue":"8","key":"2470_CR42","doi-asserted-by":"publisher","first-page":"6076","DOI":"10.1007\/s00464-022-09011-5","volume":"36","author":"H Muaddi","year":"2022","unstructured":"Muaddi H, Zhao X, Leonardelli GJ, de Mestral C, Nathens A, Stukel TA, et al. Fear of innovation: public\u2019s perception of robotic surgery. Surg Endosc. 2022;36(8):6076\u201383.","journal-title":"Surg Endosc"},{"key":"2470_CR43","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/bs.pbr.2020.06.006","volume":"253","author":"G Juravle","year":"2020","unstructured":"Juravle G, Boudouraki A, Terziyska M, Rezlescu C. Trust in artificial intelligence for medical diagnoses. Prog Brain Res. 2020;253:263\u201382.","journal-title":"Prog Brain Res"},{"key":"2470_CR44","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.ijmedinf.2019.06.027","volume":"129","author":"A Tsertsidis","year":"2019","unstructured":"Tsertsidis A, Kolkowska E, Hedstr\u00f6m K. Factors influencing seniors\u2019 acceptance of technology for ageing in place in the post-implementation stage: a literature review. Int J Med Informatics. 2019;129:324\u201333.","journal-title":"Int J Med Informatics"},{"issue":"5","key":"2470_CR45","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1016\/j.jval.2013.04.005","volume":"16","author":"PJ Lin","year":"2013","unstructured":"Lin PJ, Cangelosi MJ, Lee DW, Neumann PJ. Willingness to pay for diagnostic technologies: a review of the contingent valuation literature. Value Health: J Int Soc Pharmacoeconomics Outcomes Res. 2013;16(5):797\u2013805.","journal-title":"Value Health: J Int Soc Pharmacoeconomics Outcomes Res"},{"key":"2470_CR46","doi-asserted-by":"publisher","first-page":"e49003","DOI":"10.2196\/49003","volume":"11","author":"R van Kessel","year":"2023","unstructured":"van Kessel R, Srivastava D, Kyriopoulos I, Monti G, Novillo-Ortiz D, Milman R, et al. Digital Health reimbursement strategies of 8 European countries and Israel: scoping review and policy mapping. JMIR Mhealth Uhealth. 2023;11:e49003.","journal-title":"JMIR Mhealth Uhealth"},{"issue":"3","key":"2470_CR47","doi-asserted-by":"publisher","first-page":"e34144","DOI":"10.2196\/34144","volume":"24","author":"R Yao","year":"2022","unstructured":"Yao R, Zhang W, Evans R, Cao G, Rui T, Shen L. Inequities in Health Care services caused by the Adoption of Digital Health Technologies: scoping review. J Med Internet Res. 2022;24(3):e34144.","journal-title":"J Med Internet Res"},{"issue":"9","key":"2470_CR48","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1007\/s10198-022-01437-x","volume":"23","author":"C Steigenberger","year":"2022","unstructured":"Steigenberger C, Flatscher-Thoeni M, Siebert U, Leiter AM. Determinants of willingness to pay for health services: a systematic review of contingent valuation studies. Eur J Health Econ. 2022;23(9):1455\u201382.","journal-title":"Eur J Health Econ"},{"issue":"6975","key":"2470_CR49","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1136\/bmj.310.6975.298","volume":"310","author":"DG Altman","year":"1995","unstructured":"Altman DG, Bland JM. Statistics notes: the normal distribution. BMJ. 1995;310(6975):298.","journal-title":"BMJ"},{"issue":"3suppl","key":"2470_CR50","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.2466\/pms.1976.43.3f.1319","volume":"43","author":"LL Havlicek","year":"1976","unstructured":"Havlicek LL, Peterson NL. Robustness of the Pearson correlation against violations of assumptions. Percept Mot Skills. 1976;43(3suppl):1319\u201334.","journal-title":"Percept Mot Skills"},{"issue":"3","key":"2470_CR51","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1037\/a0028087","volume":"17","author":"AJ Bishara","year":"2012","unstructured":"Bishara AJ, Hittner JB. Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches. Psychol Methods. 2012;17(3):399\u2013417.","journal-title":"Psychol Methods"},{"key":"2470_CR52","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.jclinepi.2017.12.006","volume":"98","author":"AF Schmidt","year":"2018","unstructured":"Schmidt AF, Finan C. Linear regression and the normality assumption. J Clin Epidemiol. 2018;98:146\u201351.","journal-title":"J Clin Epidemiol"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02470-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02470-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02470-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T15:02:48Z","timestamp":1711724568000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02470-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2470"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02470-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"assertion":[{"value":"11 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures and methods performed in studies involving human participants were in accordance with the relevant guidelines and regulations, including the ethical standards of the national research committee (Hungarian Medical Research Council) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethical approval of this study was granted by the Hungarian Medical Research Council (no. IV\/5651-1\/2021\/EKU). Respondents were informed that participation in the survey was voluntary, that their data would remain anonymous, and that it would be used for scientific purposes only. Participants provided written informed consent before the start of the survey.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"87"}}