{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:46:25Z","timestamp":1770673585148,"version":"3.49.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031264375","type":"print"},{"value":"9783031264382","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The utilization of Artificial Intelligence (AI) has changed and enhanced several industries across the world, such as education, research, manufacturing and healthcare. The potential of AI to create new and enhanced applications that can benefit patients and physicians has created interest and enthusiasm, especially in a Medical Device Software (MDS) context. Although, the adoption of AI in MDS has also brought concerns for regulatory agencies and policymakers. The complexity of AI has challenged the standard requirements set by regulatory agencies, especially in the context of the differences between traditional MDS and AI. Additionally, the unique capacity of AI to continuous learning for optimal performance in real-world settings may also bring potential harm and risk to patients and physicians. The challenges discussed in this paper are in relation to: (1) <jats:italic>Software Development Life Cycle (SDLC) frameworks<\/jats:italic>; (2) <jats:italic>learning processes and adaptability of AI algorithms<\/jats:italic>; (3) <jats:italic>explainability and traceability<\/jats:italic>; and (4) <jats:italic>conflictive terminology<\/jats:italic>. At the end of this paper, conclusions and future work are presented to contribute to the safety and methodical implementation of AI in health care settings.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_13","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"163-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Challenges Associated with the Adoption of Artificial Intelligence in Medical Device Software"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0551-0897","authenticated-orcid":false,"given":"Karla Aniela","family":"Cepeda Zapata","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6173-6607","authenticated-orcid":false,"given":"Tom\u00e1s","family":"Ward","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0974-7106","authenticated-orcid":false,"given":"R\u00f3is\u00edn","family":"Loughran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-8362","authenticated-orcid":false,"given":"Fergal","family":"McCaffery","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"13_CR1","first-page":"270","volume":"1","author":"JN Kok","year":"2009","unstructured":"Kok, J.N., Boers, E.J.W., Kosters, W.A., Putten, P., van der Poel, M.: Artificial intelligence: definition, trends, techniques, and cases. Artif. Intell. 1, 270\u2013299 (2009)","journal-title":"Artif. Intell."},{"key":"13_CR2","unstructured":"European Parliamentary Research Service: EU legislation in progress artificial intelligence act (2022)"},{"key":"13_CR3","first-page":"1","volume-title":"Data Science: Concepts and Practice","author":"V Kotu","year":"2019","unstructured":"Kotu, V., Deshpande, B.: Introduction. In: Data Science: Concepts and Practice, pp. 1\u201318. (2019)"},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"94","DOI":"10.2139\/ssrn.3525037","volume":"6","author":"T Davenport","year":"2019","unstructured":"Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthc. J. 6, 94\u201398 (2019). https:\/\/doi.org\/10.2139\/ssrn.3525037","journal-title":"Future Healthc. J."},{"key":"13_CR5","unstructured":"Marketline: Global Artificial Intelligence (2021)"},{"key":"13_CR6","doi-asserted-by":"publisher","DOI":"10.5772\/intechopen.90454","volume-title":"Alginates. IntechOpen","author":"S Datta","year":"2019","unstructured":"Datta, S., Barua, R., Jonali, D.: Application of artificial intelligence in modern healthcare system. In: Pereira, L. (ed.) Alginates. IntechOpen. (2019). https:\/\/doi.org\/10.5772\/intechopen.90454"},{"key":"13_CR7","unstructured":"Food and Drug Administration (FDA): Proposed regulatory framework for modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-based software as a medical device (SaMD) - discussion paper and request for feedback (2019)"},{"key":"13_CR8","unstructured":"Reinsel, D., Gantz, J., Rydning, J.: The Digitization of the World from Edge to Core (2018)"},{"key":"13_CR9","unstructured":"Food and Drug Administration (FDA): Artificial Intelligence (AI) and Machine Learning (ML) in medical devices - executive summary for the patient engagement advisory committee meeting (2020)"},{"key":"13_CR10","unstructured":"Food and Drug Administration (FDA): Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD) Action Plan (2021)"},{"key":"13_CR11","unstructured":"IMDRF Artificial Intelligence Medical Devices (AIMD) Working Group: Machine Learning-enabled Medical Devices: Key Terms and Definitions (2022)"},{"key":"13_CR12","unstructured":"IMDRF Software as a Medical Device (SaMD) Working Group: Software as a Medical Device (SaMD): Key definitions (2013)"},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"e195","DOI":"10.1016\/S2589-7500(20)30292-2","volume":"3","author":"UJ Muehlematter","year":"2021","unstructured":"Muehlematter, U.J., Daniore, P., Vokinger, K.N.: Health policy approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015\u201320): a comparative analysis. Lancet Digit. Health. 3, e195\u2013e203 (2021). https:\/\/doi.org\/10.1016\/S2589-7500(20)30292-2","journal-title":"Lancet Digit. Health."},{"key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.1145\/581339.581406","volume-title":"Proceedings of the 24th International Conference on Software Engineering - ICSE 2002, p. 547","author":"JC Knight","year":"2002","unstructured":"Knight, J.C.: Safety critical systems. In: Proceedings of the 24th International Conference on Software Engineering - ICSE 2002, p. 547. ACM Press, New York (2002). https:\/\/doi.org\/10.1145\/581339.581406"},{"key":"13_CR15","doi-asserted-by":"publisher","DOI":"10.1017\/9781009091725","volume-title":"Introduction to Medical Software: Foundations for Digital Health, Devices, and Diagnostics","author":"X Papademetris","year":"2022","unstructured":"Papademetris, X., Quraishi, A.N., Licholai, G.P.: The FDA and software. In: Introduction to Medical Software: Foundations for Digital Health, Devices, and Diagnostics. Cambridge University Press, Cambridge (2022). https:\/\/doi.org\/10.1017\/9781009091725"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MC.1993.274940","volume":"26","author":"NG Leveson","year":"1993","unstructured":"Leveson, N.G., Turner, C.S.: An investigation of the Therac-25 accidents. Computer (Long Beach Calif.) 26, 18\u201341 (1993). https:\/\/doi.org\/10.1109\/MC.1993.274940","journal-title":"Computer (Long Beach Calif.)"},{"issue":"5","key":"13_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10664-021-09993-1","volume":"26","author":"M Haakman","year":"2021","unstructured":"Haakman, M., Cruz, L., Huijgens, H., van Deursen, A.: AI lifecycle models need to be revised. Empir. Softw. Eng. 26(5), 1\u201329 (2021). https:\/\/doi.org\/10.1007\/s10664-021-09993-1","journal-title":"Empir. Softw. Eng."},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/ICSE-SEIP.2019.00042","volume-title":"IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","author":"S Amershi","year":"2019","unstructured":"Amershi, S., et al.: Software engineering for machine learning: a case study. In: IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291\u2013300. (2019). https:\/\/doi.org\/10.1109\/ICSE-SEIP.2019.00042"},{"key":"13_CR19","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1145\/1764810.1764814","volume":"35","author":"NB Ruparelia","year":"2010","unstructured":"Ruparelia, N.B.: Software development lifecycle models. ACM SIGSOFT Softw. Eng. Notes 35, 8\u201313 (2010). https:\/\/doi.org\/10.1145\/1764810.1764814","journal-title":"ACM SIGSOFT Softw. Eng. Notes"},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.ijrobp.2018.09.038","volume":"103","author":"M Nakatsugawa","year":"2019","unstructured":"Nakatsugawa, M., et al.: The needs and benefits of continuous model updates on the accuracy of RT-induced toxicity prediction models within a learning health system. Int. J. Radiat. Oncol. Biol. Phys. 103, 460\u2013467 (2019). https:\/\/doi.org\/10.1016\/j.ijrobp.2018.09.038","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"13_CR21","unstructured":"Turpin, R., Hoefer, E., Lewelling, J., Baird, P.: Machine learning AI adapting regulatory frameworks and standards. BSI and AAMI (2020)"},{"key":"13_CR22","unstructured":"Herron, M., Gallagher, J.: White Paper on Artificial Intelligence & Medical Devices: A New Regulatory Frontier. Mason Hayes & Curran (2021)"},{"key":"13_CR23","unstructured":"COCIR: Artificial intelligence in EU medical device legislation (2020)"},{"key":"13_CR24","doi-asserted-by":"publisher","unstructured":"Lekadir, K., Quaglio Gianluca, T., Garmendia, A., Gallin, C.: Artificial intelligence in healthcare (2020).https:\/\/doi.org\/10.1016\/B978-0-12-818438-7.00013-7","DOI":"10.1016\/B978-0-12-818438-7.00013-7"},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10676-009-9187-9","volume":"11","author":"M Turilli","year":"2009","unstructured":"Turilli, M., Floridi, L.: The ethics of information transparency. Ethics Inf. Technol. 11, 105\u2013112 (2009). https:\/\/doi.org\/10.1007\/s10676-009-9187-9","journal-title":"Ethics Inf. Technol."},{"key":"13_CR26","unstructured":"Oettgen, P.: Transparency in healthcare - achieving clarity in healthcare through transparent reporting of clinical data. EBSCO Health (2017)"},{"key":"13_CR27","unstructured":"OECD: Recommendation of the council on artificial intelligence. OECD\/LEGAL\/0449 (2022)"},{"key":"13_CR28","unstructured":"High-level expert group on artificial intelligence: ethics guidelines for trustworthy AI. European Commission (2019)"},{"key":"13_CR29","unstructured":"Franca Salis, M.: A guide to Artificial Intelligence at the workplace. European Economic and Social Committee (2022)"},{"key":"13_CR30","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.cjca.2021.09.004","volume":"38","author":"J Petch","year":"2022","unstructured":"Petch, J., Di, S., Nelson, W.: Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can. J. Cardiol. 38, 204\u2013213 (2022). https:\/\/doi.org\/10.1016\/j.cjca.2021.09.004","journal-title":"Can. J. Cardiol."},{"key":"13_CR31","unstructured":"Nuffield Council on Bioethics: Artificial intelligence (AI) in healthcare and research (2018)"},{"key":"13_CR32","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.eswa.2016.06.009","volume":"62","author":"R Piltaver","year":"2016","unstructured":"Piltaver, R., Lu\u0161trek, M., Gams, M., Martin\u010di\u0107-Ip\u0161i\u0107, S.: What makes classification trees comprehensible? Expert Syst. Appl. 62, 333\u2013346 (2016). https:\/\/doi.org\/10.1016\/j.eswa.2016.06.009","journal-title":"Expert Syst. Appl."},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.csi.2013.07.012","volume":"36","author":"G Regan","year":"2013","unstructured":"Regan, G., McCaffery, F., Mc Daid, K., Flood, D.: Medical device standards\u2019 requirements for traceability during the software development lifecycle and implementation of a traceability assessment model. Comput. Stand. Interfaces 36, 3\u20139 (2013). https:\/\/doi.org\/10.1016\/j.csi.2013.07.012","journal-title":"Comput. Stand. Interfaces"},{"key":"13_CR34","doi-asserted-by":"publisher","unstructured":"Gotel, O.C.Z., Finkelstein, A.C.W.: Analysis of the requirements traceability problem. In: Proceedings of the International Conference on Requirements Engineering, pp. 94\u2013101 (1994). https:\/\/doi.org\/10.1109\/icre.1994.292398","DOI":"10.1109\/icre.1994.292398"},{"key":"13_CR35","unstructured":"Food and Drug Administration (FDA): 21 CFR 820.30 design control guidance for medical device manufacturers (1997)"},{"key":"13_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1080\/15265161.2020.1819469","volume":"20","author":"DS Char","year":"2021","unstructured":"Char, D.S., Abr\u00e0moff, M.D., Feudtner, C.: Identifying ethical considerations for machine learning healthcare applications. Am. J. Bioeth. 20, 7\u201317 (2021). https:\/\/doi.org\/10.1080\/15265161.2020.1819469","journal-title":"Am. J. Bioeth."},{"key":"13_CR37","unstructured":"Mason Hayes & Curran: Insights healthcare AI in medical devices: key challenges and global responses. https:\/\/www.mhc.ie\/latest\/insights\/ai-in-medical-devices-key-challenges-and-global-responses. Accessed 29 Aug 2022"},{"key":"13_CR38","doi-asserted-by":"publisher","first-page":"205520762210890","DOI":"10.1177\/20552076221089079","volume":"8","author":"E Niemiec","year":"2022","unstructured":"Niemiec, E.: Will the EU medical device regulation help to improve the safety and performance of medical AI devices? Digit. Health 8, 205520762210890 (2022). https:\/\/doi.org\/10.1177\/20552076221089079","journal-title":"Digit. Health"},{"key":"13_CR39","unstructured":"European Commission: Proposal for a regulation of the European parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (2021). https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX%3A52021PC0206"},{"key":"13_CR40","doi-asserted-by":"publisher","first-page":"112","DOI":"10.7202\/1077639AR","volume":"4","author":"SL Sargent","year":"2021","unstructured":"Sargent, S.L.: AI bias in healthcare: using ImpactPro as a case study for healthcare practitioners\u2019 duties to engage in anti-bias measures. Can. J. Bioeth. 4, 112\u2013116 (2021). https:\/\/doi.org\/10.7202\/1077639AR","journal-title":"Can. J. Bioeth."}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Cognitive Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26438-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:35:26Z","timestamp":1677047726000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26438-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264375","9783031264382"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26438-2_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Irish Conference on Artificial Intelligence and Cognitive Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munster","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aics2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aics2022.mtu.ie\/","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":"102","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":"41","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":"40% - 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":"3","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)"}}]}}