To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used i...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	PLOS digital health Jg. 1; H. 2; S. e0000016
Hauptverfasser:	Amann, Julia, Vetter, Dennis, Blomberg, Stig Nikolaj, Christensen, Helle Collatz, Coffee, Megan, Gerke, Sara, Gilbert, Thomas K., Hagendorff, Thilo, Holm, Sune, Livne, Michelle, Spezzatti, Andy, Strümke, Inga, Zicari, Roberto V., Madai, Vince Istvan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.02.2022 Public Library of Science (PLoS)
Schlagworte:	Algorithms Artificial intelligence Biology and Life Sciences Clinical decision making Clinical outcomes Computer and Information Sciences Coronaviruses Decision making Decision support systems Funding Human factors Machine learning Marginalized groups Medicine and Health Sciences Physical Sciences Research and Analysis Methods Social Sciences Terminology Trust
ISSN:	2767-3170, 2767-3170
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice.
AbstractList	Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice. Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice.Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice.
Author	Spezzatti, Andy Strümke, Inga Hagendorff, Thilo Gerke, Sara Holm, Sune Blomberg, Stig Nikolaj Gilbert, Thomas K. Vetter, Dennis Madai, Vince Istvan Christensen, Helle Collatz Zicari, Roberto V. Livne, Michelle Amann, Julia Coffee, Megan
AuthorAffiliation	14 Yrkeshögskolan Arcada, Helsinki, Finland 7 Digital Life Initiative, Cornell Tech, New York, NY, United States of America 1 Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland National Yang Ming Chiao Tung University, TAIWAN 6 Penn State Dickinson Law, Carlisle, PA, United States of America 12 Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway 15 Data Science Graduate School, Seoul National University, Seoul, South Korea 9 Department of Food and Resource Economics, Faculty of Science University of Copenhagen, Denmark 11 Industrial Engineering & Operations Research Department, University of California, Berkeley, United States of America 13 Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway 3 Computational Vision and Artificial Intelligence, Goethe University Frankfurt am Main, Germany 16 QUEST Center for Responsible Research, Berlin Institute
AuthorAffiliation_xml	– name: 11 Industrial Engineering & Operations Research Department, University of California, Berkeley, United States of America – name: 2 Frankfurt Big Data Lab, Goethe University Frankfurt am Main, Germany – name: 9 Department of Food and Resource Economics, Faculty of Science University of Copenhagen, Denmark – name: National Yang Ming Chiao Tung University, TAIWAN – name: 14 Yrkeshögskolan Arcada, Helsinki, Finland – name: 1 Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland – name: 12 Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway – name: 10 Google Health Research, London, United Kingdom – name: 13 Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway – name: 17 CLAIM—Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany – name: 3 Computational Vision and Artificial Intelligence, Goethe University Frankfurt am Main, Germany – name: 8 Cluster of Excellence "Machine Learning: New Perspectives for Science"—Ethics & Philosophy Lab University of Tuebingen, Germany – name: 6 Penn State Dickinson Law, Carlisle, PA, United States of America – name: 7 Digital Life Initiative, Cornell Tech, New York, NY, United States of America – name: 5 Department of Medicine and Division of Infectious Diseases and Immunology, NYU Grossman School of Medicine, New York, United States of America – name: 18 School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, United Kingdom – name: 4 University of Copenhagen, Copenhagen Emergency medical Services, Denmark – name: 15 Data Science Graduate School, Seoul National University, Seoul, South Korea – name: 16 QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Germany
Author_xml	– sequence: 1 givenname: Julia orcidid: 0000-0003-2155-5286 surname: Amann fullname: Amann, Julia – sequence: 2 givenname: Dennis orcidid: 0000-0002-5977-5535 surname: Vetter fullname: Vetter, Dennis – sequence: 3 givenname: Stig Nikolaj surname: Blomberg fullname: Blomberg, Stig Nikolaj – sequence: 4 givenname: Helle Collatz surname: Christensen fullname: Christensen, Helle Collatz – sequence: 5 givenname: Megan orcidid: 0000-0002-4581-111X surname: Coffee fullname: Coffee, Megan – sequence: 6 givenname: Sara orcidid: 0000-0002-5718-3982 surname: Gerke fullname: Gerke, Sara – sequence: 7 givenname: Thomas K. orcidid: 0000-0003-1029-4535 surname: Gilbert fullname: Gilbert, Thomas K. – sequence: 8 givenname: Thilo surname: Hagendorff fullname: Hagendorff, Thilo – sequence: 9 givenname: Sune orcidid: 0000-0002-3812-7942 surname: Holm fullname: Holm, Sune – sequence: 10 givenname: Michelle orcidid: 0000-0002-8277-4733 surname: Livne fullname: Livne, Michelle – sequence: 11 givenname: Andy orcidid: 0000-0001-7685-0448 surname: Spezzatti fullname: Spezzatti, Andy – sequence: 12 givenname: Inga orcidid: 0000-0003-1820-6544 surname: Strümke fullname: Strümke, Inga – sequence: 13 givenname: Roberto V. orcidid: 0000-0003-2792-9162 surname: Zicari fullname: Zicari, Roberto V. – sequence: 14 givenname: Vince Istvan orcidid: 0000-0002-8552-6954 surname: Madai fullname: Madai, Vince Istvan
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36812545$$D View this record in MEDLINE/PubMed
BookMark	eNp9kstuEzEUQC1UREvoHyA0Ehs2CX6MxzMsQFXFo1IlNmVt3fEjODj2YDuI7PgIvpAvwSFJ1XaBF7Zln3t07XufopMQg0HoOcELwgR5vYqbFMAvJu2WC7wbpHuEzqjoxJwRgU_u7E_Rec6ritCeYDGQJ-iUdT2hvOVn6NtNbMzPyYMLTUxNiKUptyfv_vz6fZGKs0458I0LxXjvliYoc0RgdN6Vbb1rlHfBqcppo1x2MTR5M00xlSZvczHr_Aw9tuCzOT-sM_Tlw_uby0_z688fry4vrueKY1rmI-edElYA74kC2vWgVWtNN5p-ZIJhay2BUVuuwVgt7ECUVoxqTXtBBPRshq72Xh1hJafk1pC2MoKT_w5iWkqor1LeSIoxKCVoxzhrqxkM7vRAKB4p52Nrquvt3jVtxrXRyoSSwN-T3r8J7qtcxh9yGBhhXVsFrw6CFL9vTC5y7bKq_wjBxE2WVIiBtYOo9Ay9fIAeypwlwz3v-t1cqRd3M7pN5VjTCrzZAyrFnJOxUrkCpRakJui8JFjueugol7sekoceqsHtg-Cj_79hfwFh0NNc
CitedBy_id	crossref_primary_10_1080_00207543_2024_2432463 crossref_primary_10_1109_ACCESS_2024_3409843 crossref_primary_10_1177_17562848251321915 crossref_primary_10_1186_s12889_025_22705_4 crossref_primary_10_1186_s12910_024_01079_z crossref_primary_10_3390_info15110725 crossref_primary_10_3390_make5010017 crossref_primary_10_2196_55766 crossref_primary_10_1007_s10676_023_09709_7 crossref_primary_10_1145_3733837 crossref_primary_10_1016_j_clsr_2024_105986 crossref_primary_10_3390_healthcare12040481 crossref_primary_10_1097_AS9_0000000000000423 crossref_primary_10_1016_j_chbah_2024_100066 crossref_primary_10_33457_ijhsrp_1649066 crossref_primary_10_1016_j_inffus_2023_101896 crossref_primary_10_1016_j_compbiomed_2025_110944 crossref_primary_10_1016_j_genrep_2025_102314 crossref_primary_10_3390_sym17091385 crossref_primary_10_1007_s13347_022_00598_0 crossref_primary_10_17721_2706_9699_2023_2_02 crossref_primary_10_1016_j_compbiomed_2022_106072 crossref_primary_10_1080_15265161_2025_2457716 crossref_primary_10_2196_63548 crossref_primary_10_1038_s41746_024_01270_x crossref_primary_10_2196_63733 crossref_primary_10_1016_j_apsb_2025_07_030 crossref_primary_10_38124_ijisrt_25jul620 crossref_primary_10_1080_08874417_2022_2089773 crossref_primary_10_1053_j_gastro_2025_03_005 crossref_primary_10_2196_57899 crossref_primary_10_3389_fmed_2022_894430 crossref_primary_10_1016_j_cpcardiol_2024_102454 crossref_primary_10_1016_j_fhj_2024_100171 crossref_primary_10_1007_s10462_025_11214_w crossref_primary_10_1186_s44263_024_00108_8 crossref_primary_10_1016_j_cmpb_2023_107550 crossref_primary_10_1017_S0266462324000308 crossref_primary_10_1111_jebm_12549 crossref_primary_10_1016_j_clinthera_2024_02_010 crossref_primary_10_1080_10447318_2023_2176985 crossref_primary_10_1038_s41746_025_01621_2 crossref_primary_10_1177_15553434251338433 crossref_primary_10_3389_frai_2023_1278529 crossref_primary_10_1371_journal_pdig_0000189 crossref_primary_10_3390_healthcare10101923 crossref_primary_10_1038_s41746_024_01123_7 crossref_primary_10_1007_s00146_023_01825_8 crossref_primary_10_1097_MS9_0000000000003602 crossref_primary_10_3390_geriatrics8050099 crossref_primary_10_1080_07853890_2023_2286336 crossref_primary_10_1097_TP_0000000000004627 crossref_primary_10_1136_bmjebm_2024_112919 crossref_primary_10_1016_j_pec_2025_108680 crossref_primary_10_3390_bioengineering12040375 crossref_primary_10_1016_j_artmed_2024_102815 crossref_primary_10_1016_j_eswa_2022_119296 crossref_primary_10_1088_1741_2552_acad2b crossref_primary_10_1016_j_ijmedinf_2024_105758 crossref_primary_10_1038_s41746_023_00837_4 crossref_primary_10_2196_75866 crossref_primary_10_1371_journal_pdig_0000170 crossref_primary_10_32604_cmc_2024_048408 crossref_primary_10_1038_s44259_025_00084_5 crossref_primary_10_1002_der2_241 crossref_primary_10_1093_jamia_ocae188 crossref_primary_10_3389_fdgth_2025_1555042 crossref_primary_10_3389_fmed_2023_1190429 crossref_primary_10_3390_diagnostics15172204 crossref_primary_10_1007_s11547_023_01634_5 crossref_primary_10_7759_cureus_75181 crossref_primary_10_1016_j_bulcan_2022_10_009 crossref_primary_10_1161_CIRCRESAHA_123_321765 crossref_primary_10_1016_j_techfore_2023_122768 crossref_primary_10_1097_MCC_0000000000001304 crossref_primary_10_1515_labmed_2023_0037 crossref_primary_10_1016_j_ibneur_2025_02_002 crossref_primary_10_1038_s41746_024_01006_x crossref_primary_10_1016_j_csbj_2025_05_015 crossref_primary_10_3390_diagnostics15141830 crossref_primary_10_1007_s44206_023_00063_1 crossref_primary_10_1007_s43681_024_00634_z crossref_primary_10_1016_j_ijmedinf_2024_105747 crossref_primary_10_1038_s41390_025_03980_8 crossref_primary_10_1080_14787210_2025_2484284 crossref_primary_10_1016_j_inffus_2023_101821 crossref_primary_10_2196_69333 crossref_primary_10_1007_s10620_024_08659_4 crossref_primary_10_1109_ACCESS_2025_3555543 crossref_primary_10_1002_cam4_70304 crossref_primary_10_1109_ACCESS_2025_3570643 crossref_primary_10_1080_1120009X_2025_2492960 crossref_primary_10_3389_froh_2025_1592428 crossref_primary_10_7759_cureus_59507 crossref_primary_10_1007_s43681_025_00776_8 crossref_primary_10_1016_j_eswa_2023_121186
Cites_doi	10.1007/978-3-319-99713-1 10.1038/s42256-019-0088-2 10.1016/j.jaad.2015.12.038 10.1056/NEJM199402243300807 10.1016/S2589-7500(19)30123-2 10.1145/3287560.3287569 10.1145/3375627.3375830 10.6028/NIST.IR.8312-draft 10.1002/hast.973 10.1161/CIR.0000000000000144 10.1145/3351095.3375624 10.1186/s12911-020-01332-6 10.1016/j.jbi.2020.103655 10.1007/s11023-018-9481-6 10.1016/j.artmed.2020.101901 10.1145/3313831.3376718 10.1001/jama.2018.17163 10.1016/j.artint.2021.103473 10.1001/jamanetworkopen.2020.32320 10.1145/3290605.3300831 10.1016/j.artint.2018.07.007 10.1186/s13049-015-0093-3 10.1038/s41591-020-1034-x 10.1145/3411764.3445315 10.1038/s41591-020-0942-0 10.1145/3233231 10.1002/widm.1312 10.1098/rsif.2017.0387 10.1007/s13218-020-00636-z 10.1007/978-1-4471-4474-8_22 10.1109/TTS.2021.3066209 10.1136/jamia.2010.005637 10.1109/ACCESS.2018.2870052 10.1007/s11023-019-09509-3 10.1038/s42256-019-0048-x 10.1016/j.resuscitation.2019.01.015 10.1145/3359206 10.1126/science.abg1834 10.1145/2939672.2939778 10.3389/fhumd.2021.673104 10.1145/3351095.3372827 10.1145/3375627.3375833 10.1145/3287560.3287574 10.1145/3290605.3300468 10.1038/s41591-018-0107-6 10.1145/3351095.3372850 10.1016/j.inffus.2019.12.012 10.1093/bib/bbx044
ContentType	Journal Article
Copyright	Copyright: © 2022 Amann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2022 Amann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Amann et al 2022 Amann et al
Copyright_xml	– notice: Copyright: © 2022 Amann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: 2022 Amann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 Amann et al 2022 Amann et al
CorporateAuthor	on behalf of the Z-Inspection initiative Z-Inspection initiative
CorporateAuthor_xml	– name: on behalf of the Z-Inspection initiative – name: Z-Inspection initiative
DBID	AAYXX CITATION NPM 3V. 7X7 7XB 8C1 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BENPR CCPQU COVID DWQXO FYUFA GHDGH K9. M0S PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA
DOI	10.1371/journal.pdig.0000016
DatabaseName	CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Public Health Database Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Public Health ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	Publicly Available Content Database MEDLINE - Academic CrossRef PubMed
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
DocumentTitleAlternate	Artificial intelligence explainability in clinical decision support systems
EISSN	2767-3170
ExternalDocumentID	oai_doaj_org_article_200acc72635340ffae06d9120b255b4e PMC9931364 36812545 10_1371_journal_pdig_0000016
Genre	Journal Article
GrantInformation_xml	– fundername: ; grantid: H2020-SC1-PHE-CORONAVIRUS-2020-2-RTD – fundername: ; grantid: 777107 – fundername: ; grantid: 101016233 – fundername: ; grantid: INEA/CEF/ICT/A2020/2276680, 2020-EU-IA-0098
GroupedDBID	53G 7X7 8C1 8FI 8FJ AAFWJ AAUCC AAWOE AAYXX ABUWG ACCTH AEUYN AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS BENPR CCPQU CITATION EIHBH FPL FYUFA GROUPED_DOAJ HMCUK M~E OK1 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY RPM UKHRP NPM 3V. 7XB 8FK AZQEC COVID DWQXO K9. PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c502t-b556c7f7a581ca268adc4fe6be8b3730fff1abdf5daefd7f91cdc32dd28717a83
IEDL.DBID	DOA
ISICitedReferencesCount	107
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001413701700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2767-3170
IngestDate	Mon Nov 10 04:28:55 EST 2025 Tue Nov 04 02:06:41 EST 2025 Fri Sep 05 12:45:13 EDT 2025 Tue Oct 07 07:16:59 EDT 2025 Wed Feb 19 02:25:08 EST 2025 Sat Nov 29 01:54:15 EST 2025 Tue Nov 18 21:45:37 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	Copyright: © 2022 Amann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c502t-b556c7f7a581ca268adc4fe6be8b3730fff1abdf5daefd7f91cdc32dd28717a83
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 VIM reported receiving personal fees from ai4medicine outside the submitted work. There is no connection, commercial exploitation, transfer or association between the projects of ai4medicine and the results presented in this work.
ORCID	0000-0003-1820-6544 0000-0003-2792-9162 0000-0003-2155-5286 0000-0001-7685-0448 0000-0002-4581-111X 0000-0002-8552-6954 0000-0002-8277-4733 0000-0002-3812-7942 0000-0002-5977-5535 0000-0002-5718-3982 0000-0003-1029-4535
OpenAccessLink	https://doaj.org/article/200acc72635340ffae06d9120b255b4e
PMID	36812545
PQID	3085683085
PQPubID	6980581
ParticipantIDs	doaj_primary_oai_doaj_org_article_200acc72635340ffae06d9120b255b4e pubmedcentral_primary_oai_pubmedcentral_nih_gov_9931364 proquest_miscellaneous_2779349731 proquest_journals_3085683085 pubmed_primary_36812545 crossref_citationtrail_10_1371_journal_pdig_0000016 crossref_primary_10_1371_journal_pdig_0000016
PublicationCentury	2000
PublicationDate	2022-02-01
PublicationDateYYYYMMDD	2022-02-01
PublicationDate_xml	– month: 02 year: 2022 text: 2022-02-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Francisco – name: San Francisco, CA USA
PublicationTitle	PLOS digital health
PublicationTitleAlternate	PLOS Digit Health
PublicationYear	2022
Publisher	Public Library of Science Public Library of Science (PLoS)
Publisher_xml	– name: Public Library of Science – name: Public Library of Science (PLoS)
References	pdig.0000016.ref070 B Babic (pdig.0000016.ref076) 2021; 373 AM Alaa (pdig.0000016.ref051) 2019; 32 X Liu (pdig.0000016.ref063) 2020; 26 pdig.0000016.ref074 pdig.0000016.ref073 pdig.0000016.ref072 pdig.0000016.ref071 SN Blomberg (pdig.0000016.ref057) 2019; 138 JM Durán (pdig.0000016.ref039) 2021; 47 H Kittler (pdig.0000016.ref041) 2016; 74 pdig.0000016.ref034 pdig.0000016.ref033 pdig.0000016.ref077 pdig.0000016.ref075 pdig.0000016.ref037 A Holzinger (pdig.0000016.ref048) 2019; 9 pdig.0000016.ref036 pdig.0000016.ref035 SN Blomberg (pdig.0000016.ref058) 2021; 4 EH Shortliffe (pdig.0000016.ref011) 2018; 320 T Ploug (pdig.0000016.ref023) 2020; 107 AJ London (pdig.0000016.ref029) 2019; 49 A Barredo Arrieta (pdig.0000016.ref025) 2020; 58 B-T Karsh (pdig.0000016.ref006) 2010; 17 S Wachter (pdig.0000016.ref047) 2018; 31 T. Miller (pdig.0000016.ref020) 2019; 267 AF Markus (pdig.0000016.ref017) 2021; 113 pdig.0000016.ref067 pdig.0000016.ref022 RV Zicari (pdig.0000016.ref031) 2021; 3 pdig.0000016.ref066 pdig.0000016.ref021 SM Lundberg (pdig.0000016.ref032) 2017; 30 pdig.0000016.ref065 pdig.0000016.ref064 MA Musen (pdig.0000016.ref008) 2014 A Adadi (pdig.0000016.ref014) 2018; 6 pdig.0000016.ref069 pdig.0000016.ref024 pdig.0000016.ref068 C Rudin (pdig.0000016.ref050) 2019; 1 pdig.0000016.ref052 JD Fauw (pdig.0000016.ref002) 2018; 24 P Kubben (pdig.0000016.ref001) 2019 A Jobin (pdig.0000016.ref012) 2019; 1 GD Perkins (pdig.0000016.ref061) 2015; 132 C. Rudin (pdig.0000016.ref045) 2019; 1 RV Zicari (pdig.0000016.ref056) 2021; 2 DJ Murphy (pdig.0000016.ref062) 1994; 330 B Goodman (pdig.0000016.ref026) 2017; 38 pdig.0000016.ref019 S. Robbins (pdig.0000016.ref030) 2019; 29 pdig.0000016.ref055 pdig.0000016.ref010 pdig.0000016.ref054 pdig.0000016.ref053 pdig.0000016.ref015 pdig.0000016.ref013 the Precise4Q consortium (pdig.0000016.ref018) 2020; 20 X Liu (pdig.0000016.ref003) 2019; 1 ZC Lipton (pdig.0000016.ref028) 2018; 61 P Tschandl (pdig.0000016.ref038) 2020; 26 J Wnent (pdig.0000016.ref060) 2015; 23 HP Olsen (pdig.0000016.ref027) 2019 M Langer (pdig.0000016.ref059) 2021; 296 pdig.0000016.ref009 JM Durán (pdig.0000016.ref040) 2018; 28 A Holzinger (pdig.0000016.ref049) 2020; 34 T Ching (pdig.0000016.ref005) 2018; 15 R Guidotti (pdig.0000016.ref016) 2018; 51 R Miotto (pdig.0000016.ref007) 2018; 19 pdig.0000016.ref044 pdig.0000016.ref043 pdig.0000016.ref042 pdig.0000016.ref004 pdig.0000016.ref046
References_xml	– volume-title: Fundamentals of Clinical Data Science year: 2019 ident: pdig.0000016.ref001 doi: 10.1007/978-3-319-99713-1 – volume: 1 start-page: 389 issue: 9 year: 2019 ident: pdig.0000016.ref012 article-title: The global landscape of AI ethics guidelines. publication-title: Nat Mach Intell. doi: 10.1038/s42256-019-0088-2 – volume: 74 start-page: 1093 issue: 6 year: 2016 ident: pdig.0000016.ref041 article-title: Standardization of terminology in dermoscopy/dermatoscopy: Results of the third consensus conference of the International Society of Dermoscopy publication-title: J Am Acad Dermatol doi: 10.1016/j.jaad.2015.12.038 – volume: 330 start-page: 545 issue: 8 year: 1994 ident: pdig.0000016.ref062 article-title: The Influence of the Probability of Survival on Patients’ Preferences Regarding Cardiopulmonary Resuscitation publication-title: N Engl J Med doi: 10.1056/NEJM199402243300807 – volume: 1 start-page: e271 issue: 6 year: 2019 ident: pdig.0000016.ref003 article-title: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis publication-title: Lancet Digit Health doi: 10.1016/S2589-7500(19)30123-2 – ident: pdig.0000016.ref054 doi: 10.1145/3287560.3287569 – ident: pdig.0000016.ref010 – year: 2019 ident: pdig.0000016.ref027 article-title: What’s in the Box? The Legal Requirement of Explainability in Computationally Aided Decision-Making in Public Administration publication-title: SSRN Electron J – ident: pdig.0000016.ref043 doi: 10.1145/3375627.3375830 – ident: pdig.0000016.ref022 doi: 10.6028/NIST.IR.8312-draft – volume: 38 start-page: 50 issue: 3 year: 2017 ident: pdig.0000016.ref026 article-title: European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation.” publication-title: AI Mag – volume: 49 start-page: 15 issue: 1 year: 2019 ident: pdig.0000016.ref029 article-title: Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability publication-title: Hastings Cent Rep doi: 10.1002/hast.973 – ident: pdig.0000016.ref066 – ident: pdig.0000016.ref072 – volume: 132 start-page: 1286 issue: 13 year: 2015 ident: pdig.0000016.ref061 article-title: Cardiac Arrest and Cardiopulmonary Resuscitation Outcome Reports: Update of the Utstein Resuscitation Registry Templates for Out-of-Hospital Cardiac Arrest publication-title: Circulation doi: 10.1161/CIR.0000000000000144 – ident: pdig.0000016.ref034 doi: 10.1145/3351095.3375624 – volume: 20 start-page: 310 issue: 1 year: 2020 ident: pdig.0000016.ref018 article-title: Explainability for artificial intelligence in healthcare: a multidisciplinary perspective publication-title: BMC Med Inform Decis Mak doi: 10.1186/s12911-020-01332-6 – volume: 113 start-page: 103655 year: 2021 ident: pdig.0000016.ref017 article-title: The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies publication-title: J Biomed Inform doi: 10.1016/j.jbi.2020.103655 – volume: 28 start-page: 645 issue: 4 year: 2018 ident: pdig.0000016.ref040 article-title: Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism publication-title: Minds Mach doi: 10.1007/s11023-018-9481-6 – volume: 107 start-page: 101901 year: 2020 ident: pdig.0000016.ref023 article-title: The four dimensions of contestable AI diagnostics—A patient-centric approach to explainable AI publication-title: Artif Intell Med doi: 10.1016/j.artmed.2020.101901 – ident: pdig.0000016.ref004 doi: 10.1145/3313831.3376718 – volume: 320 start-page: 2199 issue: 21 year: 2018 ident: pdig.0000016.ref011 article-title: Clinical Decision Support in the Era of Artificial Intelligence publication-title: JAMA doi: 10.1001/jama.2018.17163 – volume: 1 issue: 2 year: 2019 ident: pdig.0000016.ref050 article-title: Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition publication-title: Harv Data Sci Rev – ident: pdig.0000016.ref067 – volume: 296 start-page: 103473 year: 2021 ident: pdig.0000016.ref059 article-title: What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research publication-title: Artif Intell. doi: 10.1016/j.artint.2021.103473 – volume: 4 start-page: e2032320 issue: 1 year: 2021 ident: pdig.0000016.ref058 article-title: Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2020.32320 – ident: pdig.0000016.ref021 – ident: pdig.0000016.ref071 doi: 10.1145/3290605.3300831 – ident: pdig.0000016.ref077 – volume: 267 start-page: 1 year: 2019 ident: pdig.0000016.ref020 article-title: Explanation in artificial intelligence: Insights from the social sciences publication-title: Artif Intell doi: 10.1016/j.artint.2018.07.007 – ident: pdig.0000016.ref052 – volume: 23 start-page: 7 issue: 1 year: 2015 ident: pdig.0000016.ref060 article-title: EuReCa ONE– 27 Nations, ONE Europe, ONE Registry: a prospective observational analysis over one month in 27 resuscitation registries in Europe–the EuReCa ONE study protocol publication-title: Scand J Trauma Resusc Emerg Med doi: 10.1186/s13049-015-0093-3 – volume: 26 start-page: 1364 issue: 9 year: 2020 ident: pdig.0000016.ref063 article-title: Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension publication-title: Nat Med doi: 10.1038/s41591-020-1034-x – ident: pdig.0000016.ref036 doi: 10.1145/3411764.3445315 – volume: 26 start-page: 1229 issue: 8 year: 2020 ident: pdig.0000016.ref038 article-title: Human–computer collaboration for skin cancer recognition publication-title: Nat Med doi: 10.1038/s41591-020-0942-0 – volume: 61 start-page: 36 issue: 10 year: 2018 ident: pdig.0000016.ref028 article-title: The mythos of model interpretability publication-title: Commun ACM doi: 10.1145/3233231 – volume: 9 start-page: e1312 issue: 4 year: 2019 ident: pdig.0000016.ref048 article-title: Causability and explainability of artificial intelligence in medicine publication-title: WIREs Data Min Knowl Discov doi: 10.1002/widm.1312 – volume: 32 start-page: 11304 year: 2019 ident: pdig.0000016.ref051 article-title: Demystifying Black-box Models with Symbolic Metamodels publication-title: Adv Neural Inf Process Syst – ident: pdig.0000016.ref073 – volume: 15 start-page: 20170387 issue: 141 year: 2018 ident: pdig.0000016.ref005 article-title: Opportunities and obstacles for deep learning in biology and medicine publication-title: J R Soc Interface doi: 10.1098/rsif.2017.0387 – ident: pdig.0000016.ref035 – volume: 47 start-page: 329 issue: 5 year: 2021 ident: pdig.0000016.ref039 article-title: Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. publication-title: J Med Ethics – volume: 34 start-page: 193 issue: 2 year: 2020 ident: pdig.0000016.ref049 article-title: Measuring the Quality of Explanations: The System Causability Scale (SCS): Comparing Human and Machine Explanations publication-title: KI—Künstl Intell. doi: 10.1007/s13218-020-00636-z – start-page: 643 volume-title: Biomedical Informatics year: 2014 ident: pdig.0000016.ref008 doi: 10.1007/978-1-4471-4474-8_22 – volume: 2 start-page: 83 issue: 2 year: 2021 ident: pdig.0000016.ref056 article-title: Z-Inspection®: A Process to Assess Trustworthy AI publication-title: IEEE Trans Technol Soc doi: 10.1109/TTS.2021.3066209 – ident: pdig.0000016.ref068 – volume: 17 start-page: 617 issue: 6 year: 2010 ident: pdig.0000016.ref006 article-title: Health information technology: fallacies and sober realities publication-title: J Am Med Inform Assoc doi: 10.1136/jamia.2010.005637 – ident: pdig.0000016.ref064 – volume: 6 start-page: 52138 year: 2018 ident: pdig.0000016.ref014 article-title: Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2870052 – volume: 29 start-page: 495 issue: 4 year: 2019 ident: pdig.0000016.ref030 article-title: A Misdirected Principle with a Catch: Explicability for AI. publication-title: Minds Mach doi: 10.1007/s11023-019-09509-3 – volume: 1 start-page: 206 issue: 5 year: 2019 ident: pdig.0000016.ref045 article-title: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead publication-title: Nat Mach Intell doi: 10.1038/s42256-019-0048-x – ident: pdig.0000016.ref015 – volume: 138 start-page: 322 year: 2019 ident: pdig.0000016.ref057 article-title: Machine learning as a supportive tool to recognize cardiac arrest in emergency calls publication-title: Resuscitation doi: 10.1016/j.resuscitation.2019.01.015 – ident: pdig.0000016.ref019 – ident: pdig.0000016.ref075 doi: 10.1145/3359206 – ident: pdig.0000016.ref070 – ident: pdig.0000016.ref055 – ident: pdig.0000016.ref074 – volume: 373 start-page: 284 issue: 6552 year: 2021 ident: pdig.0000016.ref076 article-title: Beware explanations from AI in health care publication-title: Science doi: 10.1126/science.abg1834 – ident: pdig.0000016.ref033 doi: 10.1145/2939672.2939778 – ident: pdig.0000016.ref013 – volume: 3 start-page: 30 year: 2021 ident: pdig.0000016.ref031 article-title: On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls publication-title: Front Hum Dyn. doi: 10.3389/fhumd.2021.673104 – ident: pdig.0000016.ref024 doi: 10.1145/3351095.3372827 – ident: pdig.0000016.ref065 – ident: pdig.0000016.ref042 doi: 10.1145/3375627.3375833 – ident: pdig.0000016.ref044 – volume: 51 start-page: 93:1 issue: 5 year: 2018 ident: pdig.0000016.ref016 article-title: A Survey of Methods for Explaining Black Box Models publication-title: ACM Comput Surv – ident: pdig.0000016.ref069 – ident: pdig.0000016.ref046 doi: 10.1145/3287560.3287574 – ident: pdig.0000016.ref009 doi: 10.1145/3290605.3300468 – volume: 24 start-page: 1342 issue: 9 year: 2018 ident: pdig.0000016.ref002 article-title: Clinically applicable deep learning for diagnosis and referral in retinal disease publication-title: Nat Med doi: 10.1038/s41591-018-0107-6 – volume: 31 start-page: 841 issue: 2 year: 2018 ident: pdig.0000016.ref047 article-title: Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR publication-title: Harv J Law Technol Harv JOLT. 2017 – ident: pdig.0000016.ref053 doi: 10.1145/3351095.3372850 – volume: 58 start-page: 82 year: 2020 ident: pdig.0000016.ref025 article-title: Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI publication-title: Inf Fusion doi: 10.1016/j.inffus.2019.12.012 – volume: 30 start-page: 4765 year: 2017 ident: pdig.0000016.ref032 article-title: A Unified Approach to Interpreting Model Predictions. publication-title: Adv Neural Inf Process Syst – ident: pdig.0000016.ref037 – volume: 19 start-page: 1236 issue: 6 year: 2018 ident: pdig.0000016.ref007 article-title: Deep learning for healthcare: review, opportunities and challenges publication-title: Brief Bioinform doi: 10.1093/bib/bbx044
SSID	ssj0002810791
Score	2.5519989
Snippet	Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against...
SourceID	doaj pubmedcentral proquest pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	e0000016
SubjectTerms	Algorithms Artificial intelligence Biology and Life Sciences Clinical decision making Clinical outcomes Computer and Information Sciences Coronaviruses Decision making Decision support systems Funding Human factors Machine learning Marginalized groups Medicine and Health Sciences Physical Sciences Research and Analysis Methods Social Sciences Terminology Trust
SummonAdditionalLinks	– databaseName: Public Health Database dbid: 8C1 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3JbtRAEC1BQIgL-2IIqJG4mkwvdtunCCIiLkQcgpSb1WuwiGwz9iC48RF8IV9Cl902MwjBgYsPvchlV1d1VS_vATz31tk8RG2pLXVIUJzhqQ6BeSokz4URSmXOj2QT8uSkODsr38UFtz4eq5x94uiobWtwjfyAh9ggL_B52H1KkTUKd1cjhcZluELZSqBhFkd0WWNhRUhuShpvzHFJD6KCXnS2Pp-gC5HofGtGGoH7_xRt_n5ocmsWOr75v_Lfghsx_iQvpwFzGy655g5cext32O_Cx9OWuC_dhaob0q5J0w5kWEoOf3z7jj0n1AlSb8F5zk0m2O-voY7Mly6JjTw-pN90GO6TCT66vwfvj1-fHr1JIyFDarIVG1KdZbmRXqqsoEaxvFDWCO9y7QrNg6vw3lOlrc-sct5KX1JjDWfWYlomVcHvw17TNu4hEOpzqWx4Ml0IzVZaltIpl4eExUntRQJ8VkplIlo5kmZcVOMWnAxZy_TDKlRlFVWZQLr06ia0jn-0f4X6Xtoi1vZY0K7Pq2i6SNSpjJGI2sNF-EblVrktw3jTIR3TwiWwP2t8fklf_VJ3As-W6mC6uB-jGtdu-orJ4BwFcocl8GAaXIskHHHhQnSbgNwZdjui7tY09YcRHjxEnDQY2qO_i_UYrjO8yTEeQN-HvWG9cU_gqvk81P366WhHPwEQEC96 priority: 102 providerName: ProQuest
Title	To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
URI	https://www.ncbi.nlm.nih.gov/pubmed/36812545 https://www.proquest.com/docview/3085683085 https://www.proquest.com/docview/2779349731 https://pubmed.ncbi.nlm.nih.gov/PMC9931364 https://doaj.org/article/200acc72635340ffae06d9120b255b4e
Volume	1
WOSCitedRecordID	wos001413701700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: DOA dateStart: 20220101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: M~E dateStart: 20220101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: 7X7 dateStart: 20220201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: BENPR dateStart: 20220201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: 8C1 dateStart: 20220201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: PIMPY dateStart: 20220201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVATS databaseName: PLoS Journals customDbUrl: eissn: 2767-3170 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810791 issn: 2767-3170 databaseCode: FPL dateStart: 20210101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3da9RAEB-0ivgiftZoe6zgazTJJtnkSWzpUcEeQSqcT2E_NaUkxyVX6n_vbDYJORH64ss87AfZ7MxsfpPd_Q3Ae6O0ShG1-SoXGKBoSX2BwNyPGU1jGXOeaNMnm2CrVbZe58Us1Zc9E-bogd3EYXAecCmZ5UyhcWAM10Gq8jAKBIJhEWu7-iLqmQVTV_0vIwxr8nC4K0dZ-HFQzYeNqn460kKb4nz2Leop-_-FM_8-Ljn7_iyfwpMBOJLPbsDP4J6un8Oji2Fr_AVUlw3Rt5trjPRJsyV105FuKvnk236OLIJUMxbOsYFj6_6NdWS8K0nUkH6HtLuNRenEsT63L-H78uzy9Nwf8ij4MgmizhdJkkpmGE-yUPIozbiSsdGp0Jmg6OHGmJALZRLFtVHM5KFUkkZK2WiK8Yy-goO6qfVrIKFJGVcoI5HFApXAcqa5TjHO0EyY2AM6zmgpB5Jxm-viuux3zhgGG266SquHctCDB_7Ua-NINu5of2KVNbW1FNl9ARpOORhOeZfheHA0qnp8SFtSRKBpZqUH76Zq9Di7jcJr3ezaMmK4psU25ZcHh84yppFQS-eGoNQDtmcze0Pdr6mrXz2rNwLFEP3jzf94t7fwOLLXNPrT5Udw0G13-hgeypuuarcLuM_WrJcZyuw0XMCDk7NV8W3ROxHKZfEVy4ovF8WPP-aZJk0
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL0qBQEb3pRAASPBMu3ETuJkgSpeVau2IxaDNLvgZ4laJWEyA3THR_AdfBRfwnVezCAEqy7YZBE7iZ0c39xrX58D8NRqo2P02nydSgxQjGK-RMfcDzmLQxUKERnbiE3w8TiZTtO3a_C93wvj0ip7m9gYal0qN0e-zdA3iBN33Kk--k41yq2u9hIaLSwOzNlnDNnq5_uv8fs-o3T3zeTVnt-pCvgqGtG5L6MoVtxyESWBEjROhFahNbE0iWSId2ttIKS2kRbGam7TQGnFqNYutuAiYXjfC3AR7Th3KWR8yoc5HZpgMJUG3Q49xoPtDhBblc6PW6pEJ6y-9AdshAL-5N3-nqS59Nfbvf6_va8bcK3zr8mLdkDchDVT3ILLR10GwW04mZTEfKlORV6QckaKck7mw5mdH1-_uStbVg2SL9GV9lVaWvMzLCP9plKiO50iUi8qF86Qlh67vgPvzqWrd2G9KAtzD0hgYy40HqlMQklHkqfcCBNjQGa4tKEHrAdBpjo2dicKcpo1S4wco7L2hWUOOlkHHQ_84aqqZSP5R_2XDl9DXccl3pwoZ8dZZ5qcEKlQijtWIhZiH4UZxToNsM0YbsrQeLDZI6x_SJ39gpcHT4ZiNE1uvUkUplzUGeVo_EOnjebBRgvmoSXM8d6h9-4BX4H5SlNXS4r8Q0N_jh51gIbk_t-b9Riu7E2ODrPD_fHBA7hK3a6VJtl-E9bns4V5CJfUp3lezx41Y5jA-_MeBD8BDnaRfw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=To+explain+or+not+to+explain%3F-Artificial+intelligence+explainability+in+clinical+decision+support+systems&rft.jtitle=PLOS+digital+health&rft.au=Amann%2C+Julia&rft.au=Vetter%2C+Dennis&rft.au=Blomberg%2C+Stig+Nikolaj&rft.au=Christensen%2C+Helle+Collatz&rft.date=2022-02-01&rft.eissn=2767-3170&rft.volume=1&rft.issue=2&rft.spage=e0000016&rft_id=info:doi/10.1371%2Fjournal.pdig.0000016&rft_id=info%3Apmid%2F36812545&rft.externalDocID=36812545
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2767-3170&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2767-3170&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2767-3170&client=summon