{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T15:04:19Z","timestamp":1767625459079,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030680060"},{"type":"electronic","value":"9783030680077"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68007-7_6","type":"book-chapter","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T19:38:53Z","timestamp":1612294733000},"page":"96-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards Explainable Artificial Intelligence and Explanation User Interfaces to Open the \u2018Black Box\u2019 of Automated ECG Interpretation"],"prefix":"10.1007","author":[{"given":"Khaled","family":"Rjoob","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond","family":"Bond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dewar","family":"Finlay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victoria","family":"McGilligan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen J.","family":"Leslie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Rababah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleeha","family":"Iftikhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Guldenring","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Knoery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne","family":"McShane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Peace","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"6_CR1","unstructured":"The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (The IEEE Global Initiative). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, First Edition (EAD1e), Creative Commons Attribution Non-Commercial 4.0 United States License. https:\/\/ethicsinaction.ieee.org\/#read"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Wachter, S., Mittelstadt, B., Floridi, L.: Transparent, explainable, and accountable AI for robotics. Sci. Robot. (2017) https:\/\/doi.org\/10.1126\/scirobotics.aan6080","DOI":"10.1126\/scirobotics.aan6080"},{"key":"6_CR3","unstructured":"European Commission. Ethics guidelines for trustworthy AI. Retrieved from https:\/\/ec.europa.eu\/futurium\/en\/ai-allianceconsultation\/guidelines\/1"},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1016\/j.jacc.2017.07.723","volume":"70","author":"J Schl\u00e4pfer","year":"2017","unstructured":"Schl\u00e4pfer, J., Wellens, H.J.: Computer-interpreted electrocardiograms: benefits and limitations. J. Am. Coll. Cardiol. 70, 1183\u20131192 (2017). https:\/\/doi.org\/10.1016\/j.jacc.2017.07.723","journal-title":"J. Am. Coll. Cardiol."},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"S6","DOI":"10.1016\/j.jelectrocard.2018.08.007","volume":"51","author":"R Bond","year":"2018","unstructured":"Bond, R., et al.: Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J. Electrocardiol. 51, S6\u2013S11 (2018). https:\/\/doi.org\/10.1016\/j.jelectrocard.2018.08.007","journal-title":"J. Electrocardiol."},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"S86","DOI":"10.1016\/j.jelectrocard.2019.08.006","volume":"57","author":"CR Knoery","year":"2019","unstructured":"Knoery, C.R., Bond, R., et al.: SPICED-ACS: study of the potential impact of a computer-generated ECG diagnostic algorithmic certainty index in STEMI diagnosis: towards transparent AI. J. Electrocardiol. 57, S86\u2013S91 (2019). https:\/\/doi.org\/10.1016\/j.jelectrocard.2019.08.006","journal-title":"J. Electrocardiol."},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.jelectrocard.2010.07.004","volume":"43","author":"D Finlay","year":"2010","unstructured":"Finlay, D., et al.: Effects of electrode placement errors in the EASI-derived 12-lead electrocardiogram. J. Electrocardiol. 43, 606\u2013611 (2010). https:\/\/doi.org\/10.1016\/j.jelectrocard.2010.07.004","journal-title":"J. Electrocardiol."},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.jelectrocard.2016.08.009","volume":"49","author":"R Bond","year":"2016","unstructured":"Bond, R., Dewar, D., et al.: Human factors analysis of the CardioQuick Patch\u00ae: a novel engineering solution to the problem of electrode misplacement during 12-lead electrocardiogram acquisition. J. Electrocardiol. 49, 911\u2013918 (2016). https:\/\/doi.org\/10.1016\/j.jelectrocard.2016.08.009","journal-title":"J. Electrocardiol."},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.jelectrocard.2019.08.017","volume":"57","author":"K Rjoob","year":"2019","unstructured":"Rjoob, K., Bond, R., et al.: Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12-lead electrocardiogram. J. Electrocardiol. 57, 39\u201343 (2019). https:\/\/doi.org\/10.1016\/j.jelectrocard.2019.08.017","journal-title":"J. Electrocardiol."},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Rjoob, K., Bond, R.: Machine learning improves the detection of misplaced v1 and v2 electrodes during 12-lead electrocardiogram acquisition. In: Computing in Cardiology (CinC), pp. 1\u20134. IEEE (2019). https:\/\/doi.org\/10.22489\/CinC.2019.035","DOI":"10.22489\/CinC.2019.035"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.amjmed.2018.08.025","volume":"132","author":"H Smulyan","year":"2018","unstructured":"Smulyan, H.: The computerized ECG: friend and foe. Am. J. Med. 132, 153\u2013160 (2018). https:\/\/doi.org\/10.1016\/j.amjmed.2018.08.025","journal-title":"Am. J. Med."},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"1128","DOI":"10.1016\/j.jacc.2007.01.025","volume":"49","author":"JW Mason","year":"2017","unstructured":"Mason, J.W., et al.: Recommendations for the standardization and interpretation of the electrocardiogram: part II: electrocardiography diagnostic statement list a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology. J. Am. Coll. Cardiol. 49, 1128\u20131135 (2017). https:\/\/doi.org\/10.1016\/j.jacc.2007.01.025","journal-title":"J. Am. Coll. Cardiol."},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"20170821","DOI":"10.1098\/rsif.2017.0821","volume":"15","author":"A Lyon","year":"2018","unstructured":"Lyon, A., et al.: Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J. R. Soc. Interface 15, 20170821 (2018). https:\/\/doi.org\/10.1098\/rsif.2017.0821","journal-title":"J. R. Soc. Interface"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Mark Estes, N.A.: Computerized interpretation of ECGs supplement not a substitute. Circulation: Arrhythmia and Electrophysiology. Vol. 6, pp. 2\u20134 (2013) https:\/\/doi.org\/10.1161\/CIRCEP.111.000097","DOI":"10.1161\/CIRCEP.111.000097"},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Ravichandran, L., et al.: Novel tool for complete digitization of paper electrocardiography data. IEEE J. Transl. Eng. Health Med. 1, 1800107\u20131800107 (2013). https:\/\/doi.org\/10.1109\/JTEHM.2013.2262024","DOI":"10.1109\/JTEHM.2013.2262024"},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Baydoun, M., et al.: High precision digitization of paper-based ECG records: a step toward machine learning. IEEE J. Transl. Eng. Health Med. 7, 1\u20138 (2019). https:\/\/doi.org\/10.1109\/JTEHM.2019.2949784","DOI":"10.1109\/JTEHM.2019.2949784"},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"S65","DOI":"10.1016\/j.jelectrocard.2019.09.018","volume":"57","author":"R Brisk","year":"2019","unstructured":"Brisk, R., Bond, R., et al.: Deep learning to automatically interpret images of the electrocardiogram: Do we need the raw samples? J. Electrocardiol. 57, S65\u2013S69 (2019). https:\/\/doi.org\/10.1016\/j.jelectrocard.2019.09.018","journal-title":"J. Electrocardiol."},{"key":"6_CR18","unstructured":"Miller, T., et al.: Explainable AI: beware of inmates running the asylum or: how I learnt to stop worrying and love the social and behavioural sciences. arXiv preprint arXiv:1712.00547 (2017)"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T., et al.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019). https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","journal-title":"Artif. Intell."},{"key":"6_CR20","unstructured":"Lundberg, S., et al.: A unified approach to interpreting model predictions. arXiv:1705.07874 (2017)"},{"key":"6_CR21","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., et al.: \u201cWhy should i trust you?\u201d: explaining the predictions of any classifie. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon, G., et al.: Methods for interpreting and understanding deep neural networks. Digit. Sig. Proc. 73, 1\u201315 (2018). https:\/\/doi.org\/10.1016\/j.dsp.2017.10.011","journal-title":"Digit. Sig. Proc."},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-319-46307-0_29","volume-title":"Discovery Science","author":"JR Zilke","year":"2016","unstructured":"Zilke, J.R., Loza Menc\u00eda, E., Janssen, F.: DeepRED\u2013Rule Extraction from Deep Neural Networks. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 457\u2013473. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46307-0_29"},{"key":"6_CR24","doi-asserted-by":"publisher","unstructured":"Liao, Q.V., et al.: Questioning the AI: informing design practices for explainable AI user experiences. In: Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020), pp. 1\u201315 (2020). https:\/\/doi.org\/10.1145\/3313831.3376590","DOI":"10.1145\/3313831.3376590"},{"key":"6_CR25","doi-asserted-by":"publisher","unstructured":"Badashian, A.S., Mahdavi, M., Pourshirmohammadi, A.: Fundamental usability guidelines for user interface design. In: International Conference on Computational Sciences and Its Applications, pp. 106\u2013113 (2008). https:\/\/doi.org\/10.1109\/ICCSA.2008.45","DOI":"10.1109\/ICCSA.2008.45"},{"key":"6_CR26","doi-asserted-by":"publisher","first-page":"1932","DOI":"10.1016\/j.promfg.2015.07.237","volume":"3","author":"S Low","year":"2015","unstructured":"Low, S., et al.: Enhancing user experience through customisation of UI design. Procedia Manuf. 3, 1932\u20131937 (2015). https:\/\/doi.org\/10.1016\/j.promfg.2015.07.237","journal-title":"Procedia Manuf."},{"key":"6_CR27","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.jelectrocard.2017.08.007","volume":"50","author":"AW Cairns","year":"2017","unstructured":"Cairns, A.W., Bond, R.R., et al.: A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram. J. Electrocardiol. 50, 781\u2013786 (2017). https:\/\/doi.org\/10.1016\/j.jelectrocard.2017.08.007","journal-title":"J. Electrocardiol."},{"key":"6_CR28","doi-asserted-by":"publisher","unstructured":"Alqaraawi, A., et al.: Evaluating saliency map explanations for convolutional neural networks: a user study. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 275\u2013285 (2020). https:\/\/doi.org\/10.1145\/3377325.3377519","DOI":"10.1145\/3377325.3377519"},{"key":"6_CR29","doi-asserted-by":"publisher","unstructured":"Yang, F., et al. How do visual explanations foster end users\u2019 appropriate trust in machine learning?. Proceedings of the 25th International Conference on Intelligent User Interfaces March 2020, pp. 189\u2013201, https:\/\/doi.org\/10.1145\/3377325.3377480","DOI":"10.1145\/3377325.3377480"},{"key":"6_CR30","doi-asserted-by":"publisher","unstructured":"Drozdal, J., et al.: Trust in AutoML: exploring information needs for establishing trust in automated machine learning systems. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 297\u2013307 (2020). https:\/\/doi.org\/10.1145\/3377325.3377501","DOI":"10.1145\/3377325.3377501"},{"key":"6_CR31","unstructured":"DAIC (AUGUST 08, 2019). Retrieved from https:\/\/www.dicardiology.com\/content\/half-hospital-decision-makers-plan-invest-ai-2021"},{"key":"6_CR32","unstructured":"Reis, T., Bornschlegl, M.X., Hemmje, M.L.: Towards a reference model for artificial intelligence supporting big data analysis. In: Proceedings of the 2020 International Conference on Data Science (ICDATA2020) (2020)"}],"container-title":["Lecture Notes in Computer Science","Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68007-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T20:53:16Z","timestamp":1612299196000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68007-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030680060","9783030680077"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68007-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AVI-BDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"AVI Workshop on Big Data Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"avi-bda2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/avi2020.ftk.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic this event was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}