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Article: Governance of automated image analysis and artificial intelligence analytics in healthcare

TitleGovernance of automated image analysis and artificial intelligence analytics in healthcare
Authors
Issue Date2019
Citation
Clinical Radiology, 2019, v. 74, n. 5, p. 329-337 How to Cite?
Abstract© 2019 The Royal College of Radiologists The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-to-day clinical practice, but more AI applications are expected to be incorporated into the workflows in the foreseeable future. These applications could produce significant ethical and legal issues in healthcare if they cause abrupt disruptions to its contextual integrity and relational dynamics. Sustaining trust and trustworthiness is a key goal of governance, which is necessary to promote collaboration among all stakeholders and to ensure the responsible development and implementation of AI in radiology and other areas of clinical work. In this paper, the nature of AI governance in biomedicine is discussed along with its limitations. It is argued that radiologists must assume a more active role in propelling medicine into the digital age. In this respect, professional responsibilities include inquiring into the clinical and social value of AI, alleviating deficiencies in technical knowledge in order to facilitate ethical evaluation, supporting the recognition, and removal of biases, engaging the “black box” obstacle, and brokering a new social contract on informational use and security. In essence, a much closer integration of ethics, laws, and good practices is needed to ensure that AI governance achieves its normative goals.
Persistent Identifierhttp://hdl.handle.net/10722/280180
ISSN
2023 Impact Factor: 2.1
2023 SCImago Journal Rankings: 0.603
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHo, C. W.L.-
dc.contributor.authorSoon, D.-
dc.contributor.authorCaals, K.-
dc.contributor.authorKapur, J.-
dc.date.accessioned2020-01-06T02:07:36Z-
dc.date.available2020-01-06T02:07:36Z-
dc.date.issued2019-
dc.identifier.citationClinical Radiology, 2019, v. 74, n. 5, p. 329-337-
dc.identifier.issn0009-9260-
dc.identifier.urihttp://hdl.handle.net/10722/280180-
dc.description.abstract© 2019 The Royal College of Radiologists The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-to-day clinical practice, but more AI applications are expected to be incorporated into the workflows in the foreseeable future. These applications could produce significant ethical and legal issues in healthcare if they cause abrupt disruptions to its contextual integrity and relational dynamics. Sustaining trust and trustworthiness is a key goal of governance, which is necessary to promote collaboration among all stakeholders and to ensure the responsible development and implementation of AI in radiology and other areas of clinical work. In this paper, the nature of AI governance in biomedicine is discussed along with its limitations. It is argued that radiologists must assume a more active role in propelling medicine into the digital age. In this respect, professional responsibilities include inquiring into the clinical and social value of AI, alleviating deficiencies in technical knowledge in order to facilitate ethical evaluation, supporting the recognition, and removal of biases, engaging the “black box” obstacle, and brokering a new social contract on informational use and security. In essence, a much closer integration of ethics, laws, and good practices is needed to ensure that AI governance achieves its normative goals.-
dc.languageeng-
dc.relation.ispartofClinical Radiology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleGovernance of automated image analysis and artificial intelligence analytics in healthcare-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.crad.2019.02.005-
dc.identifier.pmid30898383-
dc.identifier.scopuseid_2-s2.0-85063005810-
dc.identifier.volume74-
dc.identifier.issue5-
dc.identifier.spage329-
dc.identifier.epage337-
dc.identifier.eissn1365-229X-
dc.identifier.isiWOS:000463822600001-
dc.identifier.issnl0009-9260-

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