{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:36:41Z","timestamp":1742956601202,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031727863"},{"type":"electronic","value":"9783031727870"}],"license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"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\/978-3-031-72787-0_16","type":"book-chapter","created":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T20:19:24Z","timestamp":1728764364000},"page":"163-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the\u00a0Impact of\u00a0Sociotechnical Harms in\u00a0AI-Based Medical Image Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7802-6820","authenticated-orcid":false,"given":"Emma A. M.","family":"Stanley","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7455-3383","authenticated-orcid":false,"given":"Raissa","family":"Souza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9650-5526","authenticated-orcid":false,"given":"Anthony J.","family":"Winder","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8845-360X","authenticated-orcid":false,"given":"Matthias","family":"Wilms","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8924-683X","authenticated-orcid":false,"given":"G. Bruce","family":"Pike","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5037-4292","authenticated-orcid":false,"given":"Gabrielle","family":"Dagasso","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Nielsen","sequence":"additional","affiliation":[]},{"given":"Sarah J.","family":"MacEachern","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2556-3224","authenticated-orcid":false,"given":"Nils D.","family":"Forkert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"16_CR1","unstructured":"Ethics and governance of artificial intelligence for health: WHO Guidance (2021)"},{"issue":"11","key":"16_CR2","doi-asserted-by":"publisher","first-page":"2929","DOI":"10.1038\/s41591-023-02608-w","volume":"29","author":"A Arora","year":"2023","unstructured":"Arora, A., et al.: The value of standards for health datasets in artificial intelligence-based applications. Nat. Med. 29(11), 2929\u20132938 (2023)","journal-title":"Nat. Med."},{"key":"16_CR3","unstructured":"Barocas, S., Crawford, K., Shapiro, A., Wallach, H.: The problem with bias: allocative versus representational harms in machine learning\u2019 (2017)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Camacho, M., Wilms, M., Almgren, H., Amador, K., Camicioli, R., et\u00a0al.: Exploiting macro- and micro-structural brain changes for improved Parkinson\u2019s disease classification from MRI data. npj Parkinsons Dis. 10(1), 1\u201312 (2024)","DOI":"10.1038\/s41531-024-00647-9"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Dratsch, T., et\u00a0al.: Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology 307(4), e222176 (2023)","DOI":"10.1148\/radiol.222176"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Geis, J.R., et\u00a0al.: Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 10 (2019)","DOI":"10.1186\/s13244-019-0785-8"},{"issue":"5","key":"16_CR7","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1177\/15248399211027540","volume":"23","author":"GM Hildenbrand","year":"2022","unstructured":"Hildenbrand, G.M., Perrault, E.K., Rnoh, R.H.: Patients\u2019 perceptions of health care providers\u2019 dismissive communication. Health Promot. Pract. 23(5), 777\u2013784 (2022)","journal-title":"Health Promot. Pract."},{"issue":"2","key":"16_CR8","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1111\/j.1754-9485.2009.02062.x","volume":"53","author":"N Houssami","year":"2009","unstructured":"Houssami, N., Given-Wilson, R., Ciatto, S.: Early detection of breast cancer: overview of the evidence on computer-aided detection in mammography screening. J. Med. Imaging Radiat. Oncol. 53(2), 171\u2013176 (2009)","journal-title":"J. Med. Imaging Radiat. Oncol."},{"issue":"1","key":"16_CR9","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s13195-023-01225-6","volume":"15","author":"M Klingenberg","year":"2023","unstructured":"Klingenberg, M., Stark, D., Eitel, F., Budding, C., Habes, M., et al.: Higher performance for women than men in MRI-based Alzheimer\u2019s disease detection. Alzheimer\u2019s Res. Ther. 15(1), 84 (2023)","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Kwong, J.C.C., et\u00a0al.: The silent trial - the bridge between bench-to-bedside clinical AI applications. Frontiers Digit. Health 4 (2022)","DOI":"10.3389\/fdgth.2022.929508"},{"key":"16_CR11","unstructured":"Lashbrook, A.: AI-driven dermatology could leave dark-skinned patients behind, August 2018"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Lawton, T., et\u00a0al.: Clinicians risk becoming \u2018liability sinks\u2019 for artificial intelligence. Future Healthc. J. 11(1), 100007 (2024)","DOI":"10.1016\/j.fhj.2024.100007"},{"key":"16_CR13","unstructured":"Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., et\u00a0al.: FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging. arXiv:2109.09658 [cs] (2021)"},{"issue":"5","key":"16_CR14","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1177\/1715163517725020","volume":"150","author":"R Li","year":"2017","unstructured":"Li, R.: Indigenous identity and traditional medicine: pharmacy at the crossroads. Can. Pharm. J. (Ott) 150(5), 279\u2013281 (2017)","journal-title":"Can. Pharm. J. (Ott)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"McCradden, M., Hui, K., Buchman, D.Z.: Evidence, ethics and the promise of artificial intelligence in psychiatry. J. Med. Ethics (2022). 2022-108447","DOI":"10.1136\/jme-2022-108447"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Mccradden, M., et\u00a0al.: What\u2019s fair is $$\\ldots $$ fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB. In: 2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL, USA, pp. 1505\u20131519 (2023)","DOI":"10.1145\/3593013.3594096"},{"issue":"4","key":"16_CR17","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1038\/s41591-023-02224-8","volume":"29","author":"MD McCradden","year":"2023","unstructured":"McCradden, M.D., Kirsch, R.E.: Patient wisdom should be incorporated into health AI to avoid algorithmic paternalism. Nat. Med. 29(4), 765\u2013766 (2023)","journal-title":"Nat. Med."},{"key":"16_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssmqr.2023.100321","volume":"4","author":"F McKay","year":"2023","unstructured":"McKay, F., Treanor, D., Hallowell, N.: Inalienable data: ethical imaginaries of de-identified health data ownership. SSM - Qual. Res. Health 4, 100321 (2023)","journal-title":"SSM - Qual. Res. Health"},{"issue":"11","key":"16_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0259223","volume":"16","author":"C Miani","year":"2021","unstructured":"Miani, C., Wandschneider, L., Niemann, J., Batram-Zantvoort, S., Razum, O.: Measurement of gender as a social determinant of health in epidemiology-a scoping review. PLoS ONE 16(11), e0259223 (2021)","journal-title":"PLoS ONE"},{"key":"16_CR20","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-031-16525-2_19","volume-title":"OMIA 2022","author":"C Nielsen","year":"2022","unstructured":"Nielsen, C., Tuladhar, A., Forkert, N.D.: Investigating the vulnerability of federated learning-based diabetic retinopathy grade classification to gradient inversion attacks. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) OMIA 2022. LNCS, vol. 13576, pp. 183\u2013192. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16525-2_19"},{"issue":"6464","key":"16_CR21","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464), 447\u2013453 (2019)","journal-title":"Science"},{"issue":"1","key":"16_CR22","doi-asserted-by":"publisher","first-page":"14851","DOI":"10.1038\/s41598-022-19045-3","volume":"12","author":"K Packh\u00e4user","year":"2022","unstructured":"Packh\u00e4user, K., G\u00fcndel, S., M\u00fcnster, N., Syben, C., Christlein, V., Maier, A.: Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data. Sci. Rep. 12(1), 14851 (2022)","journal-title":"Sci. Rep."},{"issue":"1","key":"16_CR23","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1177\/0846537120967349","volume":"72","author":"W Parker","year":"2021","unstructured":"Parker, W., Jaremko, J.L., Cicero, M., Azar, M., El-Emam, K., et al.: Canadian association of radiologists white paper on de-identification of medical imaging: Part 1, general principles. Can. Assoc. Radiol. J. 72(1), 13\u201324 (2021)","journal-title":"Can. Assoc. Radiol. J."},{"issue":"12","key":"16_CR24","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1097\/RLI.0000000000000707","volume":"55","author":"J Rueckel","year":"2020","unstructured":"Rueckel, J., Trappmann, L., Schachtner, B., Wesp, P., Hoppe, B.F., et al.: Impact of confounding thoracic tubes and pleural dehiscence extent on artificial intelligence pneumothorax detection in chest radiographs. Invest. Radiol. 55(12), 792\u2013798 (2020)","journal-title":"Invest. Radiol."},{"key":"16_CR25","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1007\/978-3-031-16443-9_49","volume-title":"MICCAI 2022","author":"R Selvan","year":"2022","unstructured":"Selvan, R., Bhagwat, N., Wolff Anthony, L.F., Kanding, B., Dam, E.B.: Carbon footprint of selecting and training deep learning models for medical image analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 506\u2013516. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_49"},{"issue":"12","key":"16_CR26","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B.A., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27(12), 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"issue":"8","key":"16_CR27","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1016\/j.mayocp.2019.03.026","volume":"94","author":"TD Shanafelt","year":"2019","unstructured":"Shanafelt, T.D., Schein, E., Minor, L.B., Trockel, M., Schein, P., Kirch, D.: Healing the professional culture of medicine. Mayo Clin. Proc. 94(8), 1556\u20131566 (2019)","journal-title":"Mayo Clin. Proc."},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., et\u00a0al.: Sociotechnical harms of algorithmic systems: scoping a taxonomy for harm reduction. In: Proceedings of the 2023 AAAI\/ACM Conference on AI, Ethics, and Society, AIES 2023, New York, NY, USA, pp. 723\u2013741 (2023)","DOI":"10.1145\/3600211.3604673"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Souza, R., Stanley, E.A.M., Camacho, M., Camicioli, R., et\u00a0al.: A multi-center distributed learning approach for Parkinson\u2019s disease classification using the traveling model paradigm. Front. Artif. Intell. 7 (2024)","DOI":"10.3389\/frai.2024.1301997"},{"key":"16_CR30","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-031-45249-9_28","volume-title":"CLIP EPIMI FAIMI 2023","author":"R Souza","year":"2023","unstructured":"Souza, R., Stanley, E.A.M., Forkert, N.D.: On the relationship between open science in artificial intelligence for medical imaging and global health equity. In: Wesarg, S., et al. (eds.) CLIP EPIMI FAIMI 2023. LNCS, vol. 14242, pp. 289\u2013300. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-45249-9_28"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Souza, R., Winder, A., Stanley, E.A., Vigneshwaran, V., Camacho, M., et\u00a0al.: Identifying biases in a multicenter MRI database for Parkinson\u2019s disease classification: is the disease classifier a secret site classifier? IEEE J. Biomed. Health Inf., 1\u20138 (2024)","DOI":"10.1109\/JBHI.2024.3352513"},{"issue":"4","key":"16_CR32","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10140-020-01773-6","volume":"27","author":"EM Weisberg","year":"2020","unstructured":"Weisberg, E.M., Chu, L.C., Fishman, E.K.: The first use of artificial intelligence (AI) in the ER: triage not diagnosis. Emerg. Radiol. 27(4), 361\u2013366 (2020)","journal-title":"Emerg. Radiol."},{"key":"16_CR33","unstructured":"Wu, M., et al.: Evaluation of inference attack models for deep learning on medical data (2020). http:\/\/arxiv.org\/abs\/2011.00177"}],"container-title":["Lecture Notes in Computer Science","Ethics and Fairness in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72787-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T20:22:15Z","timestamp":1728764535000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72787-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,13]]},"ISBN":["9783031727863","9783031727870"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72787-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,13]]},"assertion":[{"value":"13 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on the Ethical and Philosophical Issues in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epimi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/epimi\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}