Artificial Intelligence Applications in Health Care Practice: Scoping Review
Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications....
Saved in:
| Published in: | Journal of medical Internet research Vol. 24; no. 10; p. e40238 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Canada
Journal of Medical Internet Research
05.10.2022
Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
| Subjects: | |
| ISSN: | 1438-8871, 1439-4456, 1438-8871 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.
The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?
A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.
Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.
Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. |
|---|---|
| AbstractList | Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.BACKGROUNDArtificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood.The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?OBJECTIVEThe aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible?A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.METHODSA scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.RESULTSOf the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare.Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.CONCLUSIONSOur current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. Background: Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective: The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods: A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results: Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions: Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.Keywords: artificial intelligence; health care; implementation; scoping review; technology adoption.©Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022. Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. BackgroundArtificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. ObjectiveThe aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? MethodsA scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. ResultsOf the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. ConclusionsOur current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. |
| Audience | Academic |
| Author | Nygren, Jens M Larsson, Ingrid Svedberg, Petra Sharma, Malvika Nair, Monika Savage, Carl |
| AuthorAffiliation | 1 Department of Learning, Informatics, Management and Ethics Karolinska Institutet Medical Management Centre Stockholm Sweden 2 School of Health and Welfare Halmstad University Halmstad Sweden |
| AuthorAffiliation_xml | – name: 2 School of Health and Welfare Halmstad University Halmstad Sweden – name: 1 Department of Learning, Informatics, Management and Ethics Karolinska Institutet Medical Management Centre Stockholm Sweden |
| Author_xml | – sequence: 1 givenname: Malvika orcidid: 0000-0003-4334-9977 surname: Sharma fullname: Sharma, Malvika – sequence: 2 givenname: Carl orcidid: 0000-0003-2836-903X surname: Savage fullname: Savage, Carl – sequence: 3 givenname: Monika orcidid: 0000-0001-7610-0954 surname: Nair fullname: Nair, Monika – sequence: 4 givenname: Ingrid orcidid: 0000-0002-4341-660X surname: Larsson fullname: Larsson, Ingrid – sequence: 5 givenname: Petra orcidid: 0000-0003-4438-6673 surname: Svedberg fullname: Svedberg, Petra – sequence: 6 givenname: Jens M orcidid: 0000-0002-3576-2393 surname: Nygren fullname: Nygren, Jens M |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36197712$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-48294$$DView record from Swedish Publication Index (Högskolan i Halmstad) http://kipublications.ki.se/Default.aspx?queryparsed=id:150927543$$DView record from Swedish Publication Index (Karolinska Institutet) |
| BookMark | eNp1kttuEzEQhleoiB7oK6CVEBIIpfi0ay8XSFE4NFIEiAK3ltcebxw2dlhvWnh7nANptwL5wtb4m3_smf80O_LBQ5adY3RBcFW-YohQ8SA7wYyKkRAcH905H2enMS4QIohV-FF2TEtccY7JSTYbd72zTjvV5lPfQ9u6BryGfLxatU6r3gUfc-fzS1BtP88nqoP8c6d07zS8zq90WDnf5F_g2sHN4-yhVW2E8_1-ln17_-7r5HI0-_RhOhnPRrqsRD9i2BJDClLhAgQ32hqCas4tVrWyzApMKyOgZKioLfASK00JVbYgyNQ1ZkDPsulO1wS1kKvOLVX3Wwbl5DYQukaq9C3dgrTUasBY8LpUDAlbY0PqEigyxAheiaQ12mnFG1it64HaPvQjnUCyAjFcJP7lf_m37vt4W30-l0yQiiX6zY5O6BKMBt93qh0kDW-8m8smXMuqSPkYJ4Hne4Eu_FxD7OXSRZ3GpDyEdZSEE1zSijOU0Kf30EVYdz4NIlEUMcooqW6pRqX2OG9Dqqs3onLMS1rysig2v7z4B5WWgaXTyXnWpfgg4cUgITE9_OobtY5RTq8-Dtknd5ty6MZfVybg2Q7QXYixA3tAMJIbt8ut229nceC067eWTa917T36D3UU_Fk |
| CitedBy_id | crossref_primary_10_1089_cyber_2023_0723 crossref_primary_10_2196_59442 crossref_primary_10_7759_cureus_35237 crossref_primary_10_3390_su142416464 crossref_primary_10_1080_01616412_2024_2334118 crossref_primary_10_1177_2327857925141009 crossref_primary_10_2196_62890 crossref_primary_10_1177_10784535241239059 crossref_primary_10_2196_50903 crossref_primary_10_2196_54556 crossref_primary_10_1109_MITP_2023_3246560 crossref_primary_10_2196_53741 crossref_primary_10_1177_20552076251343752 crossref_primary_10_1016_j_ijmedinf_2024_105377 crossref_primary_10_2196_47971 crossref_primary_10_2196_47335 crossref_primary_10_3389_fpubh_2023_1285390 crossref_primary_10_2196_76973 crossref_primary_10_1016_j_socscimed_2024_117298 crossref_primary_10_2196_50342 crossref_primary_10_1016_j_jelectrocard_2024_07_001 crossref_primary_10_2196_42202 crossref_primary_10_1111_inr_70077 crossref_primary_10_1016_j_rxeng_2024_01_002 crossref_primary_10_1007_s40171_023_00356_x crossref_primary_10_2196_60148 crossref_primary_10_1007_s12553_023_00780_0 crossref_primary_10_1055_a_2521_1508 crossref_primary_10_1016_j_gerinurse_2025_03_020 crossref_primary_10_1007_s12072_024_10774_3 crossref_primary_10_1186_s40635_025_00791_3 crossref_primary_10_4103_jpbs_jpbs_1287_23 crossref_primary_10_2196_58504 crossref_primary_10_2196_53378 crossref_primary_10_2196_54985 crossref_primary_10_2196_58987 crossref_primary_10_4102_sajp_v81i1_2139 crossref_primary_10_2196_53576 crossref_primary_10_1186_s12911_025_03168_4 crossref_primary_10_2196_56924 crossref_primary_10_1007_s44174_025_00320_6 crossref_primary_10_1016_j_endien_2023_06_002 crossref_primary_10_1016_j_gerinurse_2024_10_078 crossref_primary_10_2196_71678 crossref_primary_10_3389_fdgth_2025_1550459 crossref_primary_10_3389_fendo_2025_1570811 crossref_primary_10_1007_s11096_025_01982_4 crossref_primary_10_1111_jep_70170 crossref_primary_10_3390_antibiotics14030256 crossref_primary_10_3389_frai_2025_1568886 crossref_primary_10_7759_cureus_59171 crossref_primary_10_1007_s00405_025_09578_4 crossref_primary_10_1111_jep_14237 crossref_primary_10_1371_journal_pone_0305949 crossref_primary_10_1016_j_neucom_2024_128111 crossref_primary_10_1016_j_tem_2023_08_013 crossref_primary_10_2147_JMDH_S451301 crossref_primary_10_3390_medicina61081403 crossref_primary_10_1093_jamia_ocad088 crossref_primary_10_1016_j_lana_2025_101199 crossref_primary_10_7759_cureus_51584 crossref_primary_10_1038_s41746_025_01805_w crossref_primary_10_2196_56836 crossref_primary_10_1016_j_rx_2024_01_008 crossref_primary_10_1186_s12913_025_12664_2 crossref_primary_10_2196_53888 crossref_primary_10_2196_49655 crossref_primary_10_7759_cureus_37391 crossref_primary_10_1177_24730114251352494 crossref_primary_10_2139_ssrn_5385517 crossref_primary_10_2196_43110 crossref_primary_10_1371_journal_pone_0302308 crossref_primary_10_3390_bioengineering10101109 crossref_primary_10_1007_s10742_025_00351_y crossref_primary_10_1155_jonm_3189531 crossref_primary_10_1177_14034948241307112 crossref_primary_10_2196_62865 crossref_primary_10_1186_s43058_023_00458_8 crossref_primary_10_2196_57750 crossref_primary_10_2196_55897 crossref_primary_10_7759_cureus_37023 crossref_primary_10_7759_cureus_41227 crossref_primary_10_7759_cureus_40977 crossref_primary_10_2196_50216 crossref_primary_10_2196_73995 crossref_primary_10_3390_ai6020039 crossref_primary_10_1007_s00066_025_02403_1 crossref_primary_10_1093_jamia_ocae076 crossref_primary_10_2196_46430 crossref_primary_10_1016_j_endinu_2023_02_006 crossref_primary_10_1186_s12913_024_10860_0 crossref_primary_10_2196_50130 crossref_primary_10_3390_info16040262 crossref_primary_10_1002_mcda_70001 crossref_primary_10_1186_s12955_025_02365_z |
| Cites_doi | 10.1186/s13014-021-01831-4 10.1016/j.jacr.2021.01.013 10.1108/17410401111112014 10.1001/jamasurg.2019.4917 10.1038/s41746-019-0208-8 10.1016/j.artmed.2015.09.006 10.7326/M18-0850 10.2196/15182 10.1067/j.cpradiol.2020.10.007 10.3233/SHTI190285 10.1590/0034-7167-2018-0421 10.3390/ijerph18168700 10.1111/hex.13299 10.1016/j.jbi.2017.11.011 10.3233/SHTI200312 10.1016/S0140-6736(20)30226-9 10.1108/jhr-11-2020-0535 10.2105/ajph.89.9.1322 10.1016/j.cie.2015.08.001 10.1055/s-0041-1735183 10.1016/j.hjdsi.2020.100493 10.1007/s12553-021-00555-5 10.1148/ryai.2020200024 10.1136/bmjgh-2018-000798 10.1007/s10796-021-10206-9 10.1016/j.artmed.2015.11.002 10.1016/j.artmed.2021.102158 10.1126/science.aaz3023 10.1016/j.ijmedinf.2021.104575 10.1016/j.ijmedinf.2022.104738 10.1038/s41591-018-0307-0 10.1007/s11673-020-10080-1 10.2196/34920 10.1136/jamia.1997.0040079 10.2147/IJGM.S268093 10.2196/16866 10.1055/s-0041-1741481 10.1055/s-0039-1677911 10.1067/j.cpradiol.2020.07.006 10.1038/s41433-019-0566-0 10.21106/ijma.296 10.2196/29839 10.1186/1748-5908-4-50 10.1016/j.ijmedinf.2011.10.003 10.1053/j.sodo.2021.05.011 10.1016/j.socscimed.2022.114782 10.1177/0840470419873123 10.1016/j.ijmedinf.2015.05.002 10.1093/jamia/ocab154 10.1016/j.cmi.2019.09.009 10.1093/jamia/ocaa316 10.1155/2020/9756518 10.3390/healthcare9121695 10.1055/s-0040-1715894 10.2196/32215 10.23736/S0393-2249.19.03613-0 10.1111/itor.12370 10.2196/jmir.8775 10.1007/s00330-020-06946-y 10.3389/fdgth.2021.648585 10.2196/24668 10.1590/2446-4740.180021 10.1370/afm.2518 10.17705/2msqe.00038 10.1038/s41746-021-00549-7 10.1146/annurev-pharmtox-010919-023746 10.1007/978-3-030-44289-7_4 10.1080/1364557032000119616 10.3390/ijerph182312682 10.1093/jamia/ocz192 10.1016/j.jbi.2003.09.002 10.1136/svn-2017-000101 10.2196/18599 10.1055/s-0040-1715827 10.3389/fneur.2021.643251 10.1016/j.rceng.2021.01.007 10.1002/aisy.202000052 10.1002/cac2.12215 10.1191/1478088706qp063oa 10.1287/opre.2017.1634 10.1186/s40635-019-0286-6 10.1016/j.artmed.2019.101762 10.2196/25759 10.1186/s12911-021-01488-9 10.1186/s12910-021-00577-8 10.1177/00207314211017469 10.1038/s41591-021-01614-0 10.1007/s10796-021-10146-4 10.3200/HTPS.85.1.17-26 10.1016/j.mayocp.2020.01.038 10.1016/j.ijmedinf.2019.104072 10.1016/j.ijmedinf.2012.03.002 10.1148/ryai.2019190058 10.1016/S2589-7500(20)30187-4 10.1016/j.jclepro.2021.129598 10.1287/orsc.5.1.14 10.1055/s-0040-1716748 10.1016/j.ajic.2019.06.015 10.4103/ijo.IJO_1754_19 |
| ContentType | Journal Article |
| Copyright | Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022. COPYRIGHT 2022 Journal of Medical Internet Research 2022. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022. 2022 |
| Copyright_xml | – notice: Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022. – notice: COPYRIGHT 2022 Journal of Medical Internet Research – notice: 2022. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022. 2022 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM ISN 3V. 7QJ 7RV 7X7 7XB 8FI 8FJ 8FK ABUWG AFKRA ALSLI AZQEC BENPR CCPQU CNYFK COVID DWQXO E3H F2A FYUFA GHDGH K9. KB0 M0S M1O NAPCQ PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS PRQQA 7X8 5PM AAXBQ ADTPV AOWAS D8T D8Z ZZAVC DOA |
| DOI | 10.2196/40238 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Canada ProQuest Central (Corporate) Applied Social Sciences Index & Abstracts (ASSIA) ProQuest Nursing and Allied Health Journals - PSU access expires 11/30/25. Health & Medical Collection ProQuest Central (purchase pre-March 2016) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Social Science Premium Collection ProQuest Central Essentials - QC ProQuest Central ProQuest One Community College Library & Information Science Collection Coronavirus Research Database ProQuest Central Korea Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Health & Medical Collection Library Science Database Nursing & Allied Health Premium 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 ProQuest One Social Sciences MEDLINE - Academic PubMed Central (Full Participant titles) SWEPUB Högskolan i Halmstad full text SwePub SwePub Articles SWEPUB Freely available online SWEPUB Högskolan i Halmstad SwePub Articles full text DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) Library and Information Science Abstracts (LISA) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Applied Social Sciences Index and Abstracts (ASSIA) ProQuest Central China ProQuest Central ProQuest Library Science ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Library & Information Science Collection ProQuest Central (New) Social Science Premium Collection ProQuest One Social Sciences ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Nursing & Allied Health Source ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Publicly Available Content Database |
| 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: 7RV name: Nursing & Allied Health Database url: https://search.proquest.com/nahs sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Library & Information Science |
| EISSN | 1438-8871 |
| ExternalDocumentID | oai_doaj_org_article_f3fce1187b6a408fb1d2b6e30d2d8798 oai_swepub_ki_se_450415 oai_DiVA_org_hh_48294 PMC9582911 A763676555 36197712 10_2196_40238 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Scoping Review |
| GroupedDBID | --- .4I .DC 29L 2WC 36B 53G 5GY 5VS 77I 77K 7RV 7X7 8FI 8FJ AAFWJ AAKPC AAWTL AAYXX ABDBF ABIVO ABUWG ACGFO ADBBV AEGXH AENEX AFFHD AFKRA AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS ALSLI AOIJS BAWUL BCNDV BENPR CCPQU CITATION CNYFK CS3 DIK DU5 DWQXO E3Z EAP EBD EBS EJD ELW EMB EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO ICO IEA IHR INH ISN ITC KQ8 M1O M48 NAPCQ OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PPXIY PQQKQ PRQQA RNS RPM SJN SV3 TR2 UKHRP XSB ACUHS ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QJ 7XB 8FK AZQEC COVID E3H F2A K9. PJZUB PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM AAXBQ ADRAZ ADTPV AOWAS C1A D8T D8Z O5R O5S WOQ ZZAVC |
| ID | FETCH-LOGICAL-c698t-41f2d252915e87dcfd20b77f1abaf4f8139d8e6405bfe761ac323af520dbb14e3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 108 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000869463700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1438-8871 1439-4456 |
| IngestDate | Fri Oct 03 12:40:00 EDT 2025 Tue Nov 25 03:25:10 EST 2025 Tue Nov 04 15:44:52 EST 2025 Tue Nov 04 02:07:02 EST 2025 Thu Sep 04 19:48:04 EDT 2025 Sat Nov 08 19:30:59 EST 2025 Tue Nov 11 11:11:18 EST 2025 Tue Nov 04 18:38:18 EST 2025 Thu Nov 13 16:23:12 EST 2025 Mon Jul 21 06:05:11 EDT 2025 Sat Nov 29 03:22:43 EST 2025 Tue Nov 18 21:50:02 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Keywords | technology adoption artificial intelligence health care implementation scoping review |
| Language | English |
| License | Malvika Sharma, Carl Savage, Monika Nair, Ingrid Larsson, Petra Svedberg, Jens M Nygren. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c698t-41f2d252915e87dcfd20b77f1abaf4f8139d8e6405bfe761ac323af520dbb14e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Literature Review-2 ObjectType-Feature-3 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0003-4334-9977 0000-0002-4341-660X 0000-0003-2836-903X 0000-0003-4438-6673 0000-0002-3576-2393 0000-0001-7610-0954 |
| OpenAccessLink | https://doaj.org/article/f3fce1187b6a408fb1d2b6e30d2d8798 |
| PMID | 36197712 |
| PQID | 2730434329 |
| PQPubID | 2033121 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_f3fce1187b6a408fb1d2b6e30d2d8798 swepub_primary_oai_swepub_ki_se_450415 swepub_primary_oai_DiVA_org_hh_48294 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9582911 proquest_miscellaneous_2721639740 proquest_journals_2730434329 gale_infotracmisc_A763676555 gale_infotracacademiconefile_A763676555 gale_incontextgauss_ISN_A763676555 pubmed_primary_36197712 crossref_primary_10_2196_40238 crossref_citationtrail_10_2196_40238 |
| PublicationCentury | 2000 |
| PublicationDate | 20221005 |
| PublicationDateYYYYMMDD | 2022-10-05 |
| PublicationDate_xml | – month: 10 year: 2022 text: 20221005 day: 5 |
| PublicationDecade | 2020 |
| PublicationPlace | Canada |
| PublicationPlace_xml | – name: Canada – name: Toronto – name: Toronto, Canada |
| PublicationTitle | Journal of medical Internet research |
| PublicationTitleAlternate | J Med Internet Res |
| PublicationYear | 2022 |
| Publisher | Journal of Medical Internet Research Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
| Publisher_xml | – name: Journal of Medical Internet Research – name: Gunther Eysenbach MD MPH, Associate Professor – name: JMIR Publications |
| References | ref57 ref56 ref59 ref58 ref53 ref52 ref55 ref54 ref51 ref50 ref46 ref45 Semenov, I (ref86) 2016; 228 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref100 ref101 ref40 ref35 ref34 ref37 ref36 ref31 Schlicher, J (ref75) 2021; 18 ref30 ref33 ref32 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref13 ref12 ref15 ref14 ref97 ref96 ref11 ref99 ref10 ref98 ref17 ref16 ref19 ref18 ref93 ref92 ref95 ref94 ref91 ref90 ref89 ref85 ref88 ref87 ref82 ref81 ref84 ref83 ref80 ref79 ref78 ref106 ref107 ref104 ref74 ref105 ref77 ref102 ref76 ref103 ref2 ref1 ref71 ref70 ref73 ref72 ref68 ref67 ref69 ref64 ref63 ref66 ref65 ref60 ref62 ref61 |
| References_xml | – ident: ref63 doi: 10.1186/s13014-021-01831-4 – ident: ref68 doi: 10.1016/j.jacr.2021.01.013 – ident: ref77 doi: 10.1108/17410401111112014 – ident: ref1 – ident: ref10 doi: 10.1001/jamasurg.2019.4917 – ident: ref80 doi: 10.1038/s41746-019-0208-8 – ident: ref79 doi: 10.1016/j.artmed.2015.09.006 – ident: ref40 doi: 10.7326/M18-0850 – ident: ref46 doi: 10.2196/15182 – ident: ref73 doi: 10.1067/j.cpradiol.2020.10.007 – ident: ref85 doi: 10.3233/SHTI190285 – ident: ref59 doi: 10.1590/0034-7167-2018-0421 – ident: ref45 doi: 10.3390/ijerph18168700 – ident: ref36 doi: 10.1111/hex.13299 – ident: ref103 doi: 10.1016/j.jbi.2017.11.011 – ident: ref67 doi: 10.3233/SHTI200312 – ident: ref21 doi: 10.1016/S0140-6736(20)30226-9 – ident: ref64 doi: 10.1108/jhr-11-2020-0535 – ident: ref90 doi: 10.2105/ajph.89.9.1322 – ident: ref83 doi: 10.1016/j.cie.2015.08.001 – ident: ref49 doi: 10.1055/s-0041-1735183 – ident: ref52 doi: 10.1016/j.hjdsi.2020.100493 – ident: ref35 doi: 10.1007/s12553-021-00555-5 – ident: ref76 doi: 10.1148/ryai.2020200024 – ident: ref97 doi: 10.1136/bmjgh-2018-000798 – ident: ref57 doi: 10.1007/s10796-021-10206-9 – ident: ref78 doi: 10.1016/j.artmed.2015.11.002 – ident: ref101 doi: 10.1016/j.artmed.2021.102158 – ident: ref20 doi: 10.1126/science.aaz3023 – ident: ref65 doi: 10.1016/j.ijmedinf.2021.104575 – ident: ref25 doi: 10.1016/j.ijmedinf.2022.104738 – ident: ref102 – ident: ref15 doi: 10.1038/s41591-018-0307-0 – ident: ref94 doi: 10.1007/s11673-020-10080-1 – ident: ref41 doi: 10.2196/34920 – ident: ref22 doi: 10.1136/jamia.1997.0040079 – ident: ref5 doi: 10.2147/IJGM.S268093 – ident: ref33 doi: 10.2196/16866 – ident: ref70 doi: 10.1055/s-0041-1741481 – ident: ref104 doi: 10.1055/s-0039-1677911 – ident: ref66 doi: 10.1067/j.cpradiol.2020.07.006 – ident: ref17 doi: 10.1038/s41433-019-0566-0 – ident: ref93 doi: 10.21106/ijma.296 – ident: ref12 doi: 10.2196/29839 – ident: ref107 doi: 10.1186/1748-5908-4-50 – ident: ref87 doi: 10.1016/j.ijmedinf.2011.10.003 – ident: ref71 doi: 10.1053/j.sodo.2021.05.011 – ident: ref28 doi: 10.1016/j.socscimed.2022.114782 – volume: 228 start-page: 90 year: 2016 ident: ref86 publication-title: Stud Health Technol Inform – ident: ref105 – ident: ref6 doi: 10.1177/0840470419873123 – ident: ref89 doi: 10.1016/j.ijmedinf.2015.05.002 – ident: ref47 doi: 10.1093/jamia/ocab154 – ident: ref98 doi: 10.1016/j.cmi.2019.09.009 – ident: ref48 doi: 10.1093/jamia/ocaa316 – ident: ref18 doi: 10.1155/2020/9756518 – ident: ref58 doi: 10.3390/healthcare9121695 – ident: ref74 doi: 10.1055/s-0040-1715894 – ident: ref37 doi: 10.2196/32215 – ident: ref13 doi: 10.23736/S0393-2249.19.03613-0 – ident: ref82 doi: 10.1111/itor.12370 – ident: ref91 doi: 10.2196/jmir.8775 – ident: ref53 doi: 10.1007/s00330-020-06946-y – ident: ref72 doi: 10.3389/fdgth.2021.648585 – ident: ref24 doi: 10.2196/24668 – ident: ref88 doi: 10.1590/2446-4740.180021 – ident: ref99 doi: 10.1370/afm.2518 – ident: ref55 doi: 10.17705/2msqe.00038 – ident: ref34 doi: 10.1038/s41746-021-00549-7 – ident: ref43 – ident: ref19 doi: 10.1146/annurev-pharmtox-010919-023746 – ident: ref38 doi: 10.1007/978-3-030-44289-7_4 – ident: ref39 doi: 10.1080/1364557032000119616 – ident: ref56 doi: 10.3390/ijerph182312682 – ident: ref95 doi: 10.1093/jamia/ocz192 – ident: ref23 doi: 10.1016/j.jbi.2003.09.002 – ident: ref7 doi: 10.1136/svn-2017-000101 – ident: ref31 doi: 10.2196/18599 – ident: ref54 doi: 10.1055/s-0040-1715827 – ident: ref62 doi: 10.3389/fneur.2021.643251 – ident: ref2 – ident: ref69 doi: 10.1016/j.rceng.2021.01.007 – ident: ref96 doi: 10.1002/aisy.202000052 – ident: ref16 doi: 10.1002/cac2.12215 – volume: 18 start-page: 1g issue: 4 year: 2021 ident: ref75 publication-title: Perspect Health Inf Manag – ident: ref44 doi: 10.1191/1478088706qp063oa – ident: ref81 doi: 10.1287/opre.2017.1634 – ident: ref26 doi: 10.1186/s40635-019-0286-6 – ident: ref30 doi: 10.1016/j.artmed.2019.101762 – ident: ref32 doi: 10.2196/25759 – ident: ref9 doi: 10.1186/s12911-021-01488-9 – ident: ref27 doi: 10.1186/s12910-021-00577-8 – ident: ref100 doi: 10.1177/00207314211017469 – ident: ref8 doi: 10.1038/s41591-021-01614-0 – ident: ref29 doi: 10.1007/s10796-021-10146-4 – ident: ref106 doi: 10.3200/HTPS.85.1.17-26 – ident: ref11 doi: 10.1016/j.mayocp.2020.01.038 – ident: ref51 doi: 10.1016/j.ijmedinf.2019.104072 – ident: ref84 doi: 10.1016/j.ijmedinf.2012.03.002 – ident: ref4 doi: 10.1148/ryai.2019190058 – ident: ref3 doi: 10.1016/S2589-7500(20)30187-4 – ident: ref60 doi: 10.1016/j.jclepro.2021.129598 – ident: ref92 doi: 10.1287/orsc.5.1.14 – ident: ref50 doi: 10.1055/s-0040-1716748 – ident: ref61 doi: 10.1016/j.ajic.2019.06.015 – ident: ref42 – ident: ref14 doi: 10.4103/ijo.IJO_1754_19 |
| SSID | ssj0020491 |
| Score | 2.6306522 |
| SecondaryResourceType | review_article |
| Snippet | Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and... Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected... Background: Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected... BackgroundArtificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected... |
| SourceID | doaj swepub pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e40238 |
| SubjectTerms | Application Artificial Intelligence Autonomy Boolean Clinical decision making Clinical outcomes Data Data integrity Decision making Delivery of Health Care Ethics Feasibility studies Health care Health services Humans IDC Implementation Income Information systems Information technology Innovations Medical personnel Medicine Patients Privacy Review Sampling scoping review Snowball sampling Subject heading schemes Systems technology adoption Transparency |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZgiyokxKOUEmgrg6pyimo7TuxwQVtoRaWyWvGoerPiV3cFyrb74PfjSbzLpiAu3KJ4HMWe8czYnvkGoQNWWkk9cympiE65ETyVWupUyuCbU8eJaYrBXJyLwUBeXpbDeOA2i2GVS53YKGo7MXBGfhTMLGmyIMt31zcpVI2C29VYQuMu2gCkMt5DG8cng-Hn1ZYr-L90Ez2AgOcgakccLFTHAjVA_X-q4zV7dDtWsoMo2lih00f_-_-P0cPof-J-KzBP0B1Xb6G9mL2AD3FMTwJ24bjut9Dmp3gD_xSdQ88WdQKfrcF54v7aTTge17jNb8KQ34SHMRXrbfhkk6CF2yuJbfTt9OTr-49prMiQmqKU85QHtlqWs5LmTgprvGVEC-FppSvPvQzupJWuCE6g9k4UtDIZyyqfM2K1ptxlz1CvntTuOcKZrgpPgzdYGsltQTRnRBTeyODAUatJgg6WXFImwpVD1YwfKmxbgJmqYWaC9ldk1y0-x22CY2DxqhHgtJsXk-mViqtT-cwbB4XXdVFxIr2mlunCZcQyK0UZPvIaBEQBYEYNETlX1WI2U2dfBqofFHQhijzPE_QmEvlJ-FNTxQSHMF7A2OpQ7nYow4o23ealCKmoUWbqt_wk6NWqGXpClFztJgugYTChgofZ22nFdjXuLOyUhaAsQaIj0J2J6bbU41GDN17mMnCcBo60ot_p8mF80W_mcjRSPNDxBB3-hSy--h6enOI5AEO8-PcwX6L7DFJNmmCNXdSbTxduD90zP-fj2XQ_LvRfmAtexg priority: 102 providerName: ProQuest |
| Title | Artificial Intelligence Applications in Health Care Practice: Scoping Review |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36197712 https://www.proquest.com/docview/2730434329 https://www.proquest.com/docview/2721639740 https://pubmed.ncbi.nlm.nih.gov/PMC9582911 https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-48294 http://kipublications.ki.se/Default.aspx?queryparsed=id:150927543 https://doaj.org/article/f3fce1187b6a408fb1d2b6e30d2d8798 |
| Volume | 24 |
| WOSCitedRecordID | wos000869463700001&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: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: DOA dateStart: 19990101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Library Science Database customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: M1O dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/libraryscience providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: 7RV dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fb9MwELZgoAkJIRi_Altl0DSeotmOEzu8dbCJSmupBlTlyYodm1agFK0tfz9nx62agcQLL1Zkn6Pk7myf5fs-I3TMylpSx2xKKqJTbgRPpZY6lRJic2o5MeEymMmlGI3kdFqOd6768jlhLT1wq7hTlzlj_Z3Yuqg4kU7TmunCZqRmtRRlgPlC1LPZTMWtFsS9dB_d94nO4GKn3K9MnZUnEPT_OQ3vrEM3cyQ7TKJh9bl4iB7EsBH32899hG7Z5gAdRdABPsERVeS1jONwPUD7w3hw_hhd-p4tWQQe7LBw4v7OATaeN7iFJWEPS8LjiKB6C68MuCrcniQ8QV8uzj-_-5DGixRSU5RylXKwRs1yVtLcSlEbVzOihXC00pXjTkIUWEtbQOymnRUFrUzGssrljNRaU26zp2ivWTT2OcKZrgpHIYgrjeR1QTRnRBTOSIi7aK1Jgo43SlYmsoz7yy5-KNhteFuoYIsE9bZiP1tajZsCZ95C20bPgh0qwDdU9A31L99I0GtvX-V5LhqfSPOtWi-XavBppPowrxaiyPM8QW-ikFvAl5oq4hLgfz01VkfysCMJA9F0mzdupOJEsFQQHZIA3i0T9Grb7Hv65LbGLtZehnmFCg7ae9Z63fa_M9jgCkFZgkTHHzuK6bY081mgCS9zCRanYJHWcztd3s8n_aDL2UxxkOMJOvmLWKz6Dk9W8dzzObz4H5Z5ie4xjyMJmRiHaG91vbZH6K75tZovr3votria-HIqQil76M7Z-Wh81QujHMoh_Qh148Fw_PU3imVW9g |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1R3bbtMw1BoDDSTEZdwK2zBojKdoseMkDhJChTGtWldNbFR7M7FjrxUoHe0K4qf4Rs5J3NIMxNseeIviYys-Ptf4XAjZ5FkhmeM2CPNQB8KkIpBa6kBKsM2ZFaGpmsH0u2mvJ09OssMl8nOWC4NhlTOZWAnqYmTwH_k2qNmwyoLM3px9DbBrFN6uzlpo1GSxb398B5dt8rqzA-f7gvPd98fv9gLfVSAwSSbPAwGfVvCYZyy2Mi2MK3io09SxXOdOOAkmUSFtAoaMdhac_NxEPMpdzMNCayZsBOteIVdBjjMMIUs_9OcOHljbbIXcxPBqIOxtgfqwoe-qtgB_Cv8F7XcxMrNRv7TSebu3_zds3SG3vHVN2zU73CVLtlwl6z43g25Rn3yFxEi9VFslKwc-vuAe6eLMuqYG7SwUK6XthXt-Oixpnb1FMXuLHvpEs1ewZJV-RusLl_vk46Xs9QFZLkelfURopPPEMbB1MyNFkYRa8DBNnJFgnrJChy2yOaMKZXwxduwJ8kWBU4bEoyriaZGNOdhZXX3kIsBbJKn5IBYLr16MxqfKyx7lImcstpXXSS5C6TQruE5sFBa8kGkGizxHglRYDqTEeKPTfDqZqM5RT7VB_SRpEsdxi7z0QG4EX2pyn74B-8UKYg3ItQYkyCvTHJ6RrPLycqJ-02uLPJsP40yMASztaIowHBGaCsDew5pN5vuOEgaODOMtkjYYqIGY5kg5HFTV1LNYwokzOJGa1RpTdob9doXLwUAJgBMtsvUXMP_qMzxZJWIse_H439t8Sq7vHR90VbfT239CbnBMqqnCUtbI8vl4atfJNfPtfDgZb1QihpJPl82lvwDOg7vZ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLbGhiokxGXcCtswaIynqLHjJA4SQoVSUa2rKsGm8WTi21qB0tELiL_Gr-M4cUszEG974K2Kj63Y_s6tOReE9mmmObHUBGEeyoCplAVcchlwDrY5MSxUZTOYk346GPDT02y4gX4uc2FcWOVSJpaCWk-U-4-8BWo2LLMgs5b1YRHDTvfV-dfAdZByX1qX7TQqiByaH9_BfZu97HXgrp9R2n374c27wHcYCFSS8XnA4DU1jWlGYsNTraymoUxTS3KZW2Y5mEeamwSMGmkNOPy5imiU25iGWkrCTATrXkFbYJIz4LGtYe9o-HHl7oHtTRrougu2Bpi3mNOONe1XNgn4UxWs6cKLcZq1aqalBuze_J_P7ha64e1u3K4Y5TbaMMU22vVZG_gA-7QsB1Ps5d02ahz5yIM7qO9mVtU2cG-tjClur0UA4HGBq7wu7PK68NCnoL2AJcvENFx9irmLji9lr_fQZjEpzAOEI5knloAVnCnOdBJKRsM0sYqD4Uq0DJtof4kQoXyZdtct5IsAd80BSZRAaqK9Fdl5VZfkIsFrB6_VoCsjXj6YTM-El0rCRlYZ13BeJjkLuZVEU5mYKNRU8zSDRZ46cApXKKRwsDnLF7OZ6L0fiDYopiRN4jhuoueeyE7gTVXuEztgv662WI1yp0YJkkzVh5fwFV6SzsRv7DbRk9Wwm-miAwszWTga6g40ZXB69yuWWe07Sgi4OIQ2UVpjptrB1EeK8aiss57FHG6cwI1UbFeb0hmftMuzHI0EAzrWRAd_IfOPPsMvI1jsCmI8_Pc2H6MGMKfo9waHj9A16rJtyniVHbQ5ny7MLrqqvs3Hs-melzcYfbpsNv0Fxf_F_A |
| 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=Artificial+Intelligence+Applications+in+Health+Care+Practice%3A+Scoping+Review&rft.jtitle=Journal+of+medical+Internet+research&rft.au=Sharma%2C+Malvika&rft.au=Savage%2C+Carl&rft.au=Nair%2C+Monika&rft.au=Larsson%2C+Ingrid&rft.date=2022-10-05&rft.eissn=1438-8871&rft.volume=24&rft.issue=10&rft.spage=e40238&rft_id=info:doi/10.2196%2F40238&rft_id=info%3Apmid%2F36197712&rft.externalDocID=36197712 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1438-8871&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1438-8871&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1438-8871&client=summon |