{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:26:39Z","timestamp":1776093999966,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031958373","type":"print"},{"value":"9783031958380","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-95838-0_14","type":"book-chapter","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:14:47Z","timestamp":1750590887000},"page":"140-149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Conformal Prediction for\u00a0ECG Interpretation: A Study on\u00a0Human-AI Collaboration in\u00a0Clinical Decision Support"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8481-6079","authenticated-orcid":false,"given":"Duarte","family":"Folgado","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1934-5899","authenticated-orcid":false,"given":"Lorenzo","family":"Famiglini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0027-5157","authenticated-orcid":false,"given":"Andrea","family":"Campagner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4736-5221","authenticated-orcid":false,"given":"H\u00e9lder","family":"Dores","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9445-4809","authenticated-orcid":false,"given":"Mar\u00edlia","family":"Barandas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-7424","authenticated-orcid":false,"given":"Hugo","family":"Gamboa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4065-3415","authenticated-orcid":false,"given":"Federico","family":"Cabitza","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Babbar, V., Bhatt, U., Weller, A.: On the utility of prediction sets in human-ai teams. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pp. 2457\u20132463. International Joint Conferences on Artificial Intelligence Organization, Vienna (2022)","DOI":"10.24963\/ijcai.2022\/341"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Bertrand, A., Belloum, R., Eagan, J.R., Maxwell, W.: How cognitive biases affect XAI-assisted decision-making: a systematic review. In: Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 78\u201391. ACM (2022)","DOI":"10.1145\/3514094.3534164"},{"issue":"1","key":"14_CR3","doi-asserted-by":"publisher","first-page":"269","DOI":"10.3390\/make5010017","volume":"5","author":"F Cabitza","year":"2023","unstructured":"Cabitza, F., Campagner, A., Natali, C., Parimbelli, E., Ronzio, L., Cameli, M.: Painting the black box white: experimental findings from applying XAI to an ECG reading setting. Mach. Learn. Knowl. Extract. 5(1), 269\u2013286 (2023)","journal-title":"Mach. Learn. Knowl. Extract."},{"key":"14_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102506","volume":"138","author":"F Cabitza","year":"2023","unstructured":"Cabitza, F., et al.: Rams, hounds and white boxes: investigating human\u2013AI collaboration protocols in medical diagnosis. Artif. Intell. Med. 138, 102506 (2023)","journal-title":"Artif. Intell. Med."},{"key":"14_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2021.102696","volume":"155","author":"F Cabitza","year":"2021","unstructured":"Cabitza, F., Campagner, A., Simone, C.: The need to move away from agential-AI: empirical investigations, useful concepts and open issues. Int. J. Hum Comput Stud. 155, 102696 (2021)","journal-title":"Int. J. Hum Comput Stud."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Cabitza, F., Fregosi, C., Campagner, A., Natali, C.: Explanations considered harmful: the impact of misleading explanations on accuracy in hybrid human-ai decision making. In: World Conference on Explainable Artificial Intelligence, pp. 255\u2013269. Springer, Heidelberg (2024)","DOI":"10.1007\/978-3-031-63803-9_14"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Cox, A.L., Gould, S.J., Cecchinato, M.E., Iacovides, I., Renfree, I.: Design frictions for mindful interactions: the case for microboundaries. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1389\u20131397 (2016)","DOI":"10.1145\/2851581.2892410"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Dahlb\u00e4ck, N., J\u00f6nsson, A., Ahrenberg, L.: Wizard of Oz studies: why and how. In: Proceedings of the 1st International Conference on Intelligent User Interfaces, pp. 193\u2013200 (1993)","DOI":"10.1145\/169891.169968"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Ehsan, U., Riedl, M.O.: Explainability pitfalls: beyond dark patterns in explainable AI. Patterns 5(6) (2024)","DOI":"10.1016\/j.patter.2024.100971"},{"issue":"17","key":"14_CR10","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1001\/jama.298.17.2072","volume":"298","author":"DG Federman","year":"2007","unstructured":"Federman, D.G., Chanko, E.H.: Differential diagnosis in internal medicine: from symptom to diagnosis. JAMA 298(17), 2070\u20132075 (2007)","journal-title":"JAMA"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Gaube, S., et al.: Do as AI say: susceptibility in deployment of clinical decision-aids. npj Dig. Med. 4(1), 1\u20138 (2021)","DOI":"10.1038\/s41746-021-00385-9"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Kompa, B., Snoek, J., Beam, A.L.: Second opinion needed: communicating uncertainty in medical machine learning. npj Dig. Med. 4(1), 1\u20136 (2021)","DOI":"10.1038\/s41746-020-00367-3"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Li, B., et\u00a0al.: Conversational AI in health: design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM. In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pp. 1\u201310 (2024)","DOI":"10.1145\/3613905.3651891"},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Michalowski, M., et al.: Manually-curated versus LLM-generated explanations for complex patient cases: an exploratory study with physicians. In: International Conference on Artificial Intelligence in Medicine, pp. 313\u2013323. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-66535-6_33","DOI":"10.1007\/978-3-031-66535-6_33"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Naiseh, M., Al-Mansoori, R.S., Al-Thani, D., Jiang, N., Ali, R.: Nudging through friction: an approach for calibrating trust in explainable AI. In: 2021 8th International Conference on Behavioral and Social Computing, pp.\u00a01\u20135. IEEE (2021)","DOI":"10.1109\/BESC53957.2021.9635271"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Nourani, M., et al.: Anchoring bias affects mental model formation and user reliance in explainable AI systems. In: 26th International Conference on Intelligent User Interfaces, pp. 340\u2013350. Association for Computing Machinery (2021)","DOI":"10.1145\/3397481.3450639"},{"key":"14_CR17","first-page":"777","volume":"70","author":"P Ohm","year":"2018","unstructured":"Ohm, P., Frankle, J.: Desirable inefficiency. Fla. L. Rev. 70, 777 (2018)","journal-title":"Fla. L. Rev."},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, D., Chatzimparmpas, A., Kamali, N., Hullman, J.: Evaluating the utility of conformal prediction sets for AI-advised image labeling. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1\u201319 (2024)","DOI":"10.1145\/3613904.3642446"}],"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\/978-3-031-95838-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:14:50Z","timestamp":1750590890000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95838-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031958373","9783031958380"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95838-0_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 June 2025","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":"Pavia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime25.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}