{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:20:51Z","timestamp":1750591251065,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031665349"},{"type":"electronic","value":"9783031665356"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-66535-6_28","type":"book-chapter","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T12:03:45Z","timestamp":1721995425000},"page":"257-266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Do You Trust Your Model Explanations? An Analysis of XAI Performance Under Dataset Shift"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4922-5641","authenticated-orcid":false,"given":"Lorenzo","family":"Peracchio","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7007-0862","authenticated-orcid":false,"given":"Giovanna","family":"Nicora","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2887-088X","authenticated-orcid":false,"given":"Tommaso Mario","family":"Buonocore","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-9808","authenticated-orcid":false,"given":"Riccardo","family":"Bellazzi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-828X","authenticated-orcid":false,"given":"Enea","family":"Parimbelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102471","volume":"135","author":"E Parimbelli","year":"2023","unstructured":"Parimbelli, E., Buonocore, T.M., Nicora, G., Michalowski, W., Wilk, S., Bellazzi, R.: Why did AI get this one wrong? \u2014 Tree-based explanations of machine learning model predictions. Artif. Intell. Med. 135, 102471 (2023). https:\/\/doi.org\/10.1016\/j.artmed.2022.102471","journal-title":"Artif. Intell. Med."},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"El Shawi, R., Sherif, Y., Al-Mallah, M., Sakr, S.: Interpretability in healthcare a comparative study of local machine learning interpretability techniques. In: Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), p. 275\u201380 (2019). https:\/\/doi.org\/10.1109\/CBMS.2019.00065","DOI":"10.1109\/CBMS.2019.00065"},{"key":"28_CR3","doi-asserted-by":"publisher","unstructured":"Brankovic, A., Cook, D., Rahman, J., Huang, W., Khanna, S.: Evaluation of popular XAI applied to clinical prediction models: can they be trusted? (2023). https:\/\/doi.org\/10.48550\/arXiv.2306.11985","DOI":"10.48550\/arXiv.2306.11985"},{"key":"28_CR4","doi-asserted-by":"publisher","unstructured":"Krishna, S., Han, T., Gu, A., Pombra, J., Jabbari, S., Wu, S., et al.: The disagreement problem in explainable machine learning: a practitioner\u2019s perspective (2022). https:\/\/doi.org\/10.48550\/arXiv.2202.01602","DOI":"10.48550\/arXiv.2202.01602"},{"key":"28_CR5","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1001\/jamainternmed.2021.2626","volume":"181","author":"A Wong","year":"2021","unstructured":"Wong, A., Otles, E., Donnelly, J.P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., et al.: External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern. Med. 181, 1065\u20131070 (2021). https:\/\/doi.org\/10.1001\/jamainternmed.2021.2626","journal-title":"JAMA Intern. Med."},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","volume":"45","author":"JG Moreno-Torres","year":"2012","unstructured":"Moreno-Torres, J.G., Raeder, T., Alaiz-Rodr\u00edguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern Recogn. 45, 521\u2013530 (2012). https:\/\/doi.org\/10.1016\/j.patcog.2011.06.019","journal-title":"Pattern Recogn."},{"key":"28_CR7","doi-asserted-by":"publisher","unstructured":"Peracchio, L., Nicora, G., Parimbelli, E., Buonocore, T.M., Bergamaschi, R., Tavazzi, E., et al.: Evaluation of predictive reliability to foster trust in artificial intelligence. A case study in multiple sclerosis (2024). https:\/\/doi.org\/10.48550\/arXiv.2402.17554","DOI":"10.48550\/arXiv.2402.17554"},{"key":"28_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.103996","volume":"127","author":"G Nicora","year":"2022","unstructured":"Nicora, G., Rios, M., Abu-Hanna, A., Bellazzi, R.: Evaluating pointwise reliability of machine learning prediction. J. Biomed. Inform. 127, 103996 (2022). https:\/\/doi.org\/10.1016\/j.jbi.2022.103996","journal-title":"J. Biomed. Inform."},{"key":"28_CR9","first-page":"925","volume":"2020","author":"G Nicora","year":"2021","unstructured":"Nicora, G., Bellazzi, R.: A reliable machine learning approach applied to single-cell classification in acute myeloid leukemia. AMIA Annu. Symp. Proc. 2020, 925\u2013932 (2021)","journal-title":"AMIA Annu. Symp. Proc."},{"key":"28_CR10","doi-asserted-by":"publisher","first-page":"2454","DOI":"10.1182\/blood-2012-03-420489","volume":"120","author":"PL Greenberg","year":"2012","unstructured":"Greenberg, P.L., Tuechler, H., Schanz, J., Sanz, G., Garcia-Manero, G., Sol\u00e9, F., et al.: Revised international prognostic scoring system for myelodysplastic syndromes. Blood 120, 2454\u20132465 (2012). https:\/\/doi.org\/10.1182\/blood-2012-03-420489","journal-title":"Blood"},{"key":"28_CR11","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1182\/asheducation-2002.1.136","volume":"2002","author":"PL Greenberg","year":"2002","unstructured":"Greenberg, P.L., Young, N.S., Gattermann, N.: Myelodysplastic syndromes. Hematology 2002, 136\u2013161 (2002). https:\/\/doi.org\/10.1182\/asheducation-2002.1.136","journal-title":"Hematology"},{"key":"28_CR12","doi-asserted-by":"publisher","first-page":"9423","DOI":"10.3390\/app12199423","volume":"12","author":"P Lopes","year":"2022","unstructured":"Lopes, P., Silva, E., Braga, C., Oliveira, T., Rosado, L.: XAI systems evaluation: a review of human and computer-centred methods. Appl. Sci. 12, 9423 (2022). https:\/\/doi.org\/10.3390\/app12199423","journal-title":"Appl. Sci."},{"key":"28_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.688969","volume":"4","author":"V Belle","year":"2021","unstructured":"Belle, V., Papantonis, I.: Principles and practice of explainable machine learning. Front Big Data 4, 688969 (2021). https:\/\/doi.org\/10.3389\/fdata.2021.688969","journal-title":"Front Big Data"},{"key":"28_CR14","doi-asserted-by":"publisher","first-page":"498","DOI":"10.3390\/electronics13030498","volume":"13","author":"G Joshi","year":"2024","unstructured":"Joshi, G., Jain, A., Araveeti, S.R., Adhikari, S., Garg, H., Bhandari, M.: FDA-approved artificial intelligence and machine learning (AI\/ML)-enabled medical devices: an updated landscape. Electronics 13, 498 (2024). https:\/\/doi.org\/10.3390\/electronics13030498","journal-title":"Electronics"},{"key":"28_CR15","unstructured":"World Health Organization: Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization (2021). https:\/\/iris.who.int\/handle\/10665\/341996"},{"key":"28_CR16","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019). https:\/\/doi.org\/10.1186\/s12916-019-1426-2","journal-title":"BMC Med."},{"key":"28_CR17","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-66535-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T12:11:00Z","timestamp":1721995860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-66535-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031665349","9783031665356"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-66535-6_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salt Lake City, UT","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime24.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}