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Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners

Tinotenda Masawi, Edward Miller, Daniel Rees Orcid Logo, Roderick Thomas

Journal of Decision Systems, Volume: 34, Issue: 1

Swansea University Authors: Tinotenda Masawi, Edward Miller, Daniel Rees Orcid Logo, Roderick Thomas

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DOI (Published version): 10.1080/12460125.2025.2458874

Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming healthcare, offering both opportunities and challenges. This study examines the perceptions of healthcare practitioners in Wales regarding AI’s role in diagnostics. Through semi-structured interviews with 10 expert practitioners f...

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Published in: Journal of Decision Systems
ISSN: 1246-0125 2116-7052
Published: Informa UK Limited 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68743
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spelling 2025-02-05T14:35:06.3866321 v2 68743 2025-01-27 Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners 45b086efb459f94bf3228ad930d6ff92 Tinotenda Masawi Tinotenda Masawi true false 4c3e136bac76a97b77b96713fc1b810b Edward Miller Edward Miller true false daa6762111f9ebf62b9c2ec655512783 0000-0003-0372-6096 Daniel Rees Daniel Rees true false 891091891b6eee412668ae216f713312 Roderick Thomas Roderick Thomas true false 2025-01-27 The rapid advancement of Artificial Intelligence (AI) is transforming healthcare, offering both opportunities and challenges. This study examines the perceptions of healthcare practitioners in Wales regarding AI’s role in diagnostics. Through semi-structured interviews with 10 expert practitioners from various specializations, it uncovers diverse views shaped by experience and second-hand knowledge. While AI is recognized for enhancing diagnostic accuracy and administrative efficiency, concerns persist about the loss of human touch, data security, and biases. A key finding is the unanimous call for comprehensive AI training to bridge knowledge gaps and build confidence. Using an interpretivist qualitative approach, with purposive sampling and thematic analysis, the study highlights nuanced practitioner perspectives. The findings underscore the need for equitable AI resource distribution and tailored training to address geographical disparities. The study advocates for future research with larger, more diverse samples and follow-up evaluations to assess AI training’s long-term impact on healthcare practice. Journal Article Journal of Decision Systems 34 1 Informa UK Limited 1246-0125 2116-7052 Artificial intelligence; innovation; healthcare; clinician; information systems; UTAUT 4 2 2025 2025-02-04 10.1080/12460125.2025.2458874 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-02-05T14:35:06.3866321 2025-01-27T10:54:16.6623929 Faculty of Humanities and Social Sciences School of Management - Business Management Tinotenda Masawi 1 Edward Miller 2 Daniel Rees 0000-0003-0372-6096 3 Roderick Thomas 4 68743__33510__9e0e1c02c12a40d783bf04f55c0f5338.pdf 68743.VOR.pdf 2025-02-05T14:28:32.9677207 Output 2072435 application/pdf Version of Record true © 2025 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/
title Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
spellingShingle Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
Tinotenda Masawi
Edward Miller
Daniel Rees
Roderick Thomas
title_short Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
title_full Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
title_fullStr Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
title_full_unstemmed Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
title_sort Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
author_id_str_mv 45b086efb459f94bf3228ad930d6ff92
4c3e136bac76a97b77b96713fc1b810b
daa6762111f9ebf62b9c2ec655512783
891091891b6eee412668ae216f713312
author_id_fullname_str_mv 45b086efb459f94bf3228ad930d6ff92_***_Tinotenda Masawi
4c3e136bac76a97b77b96713fc1b810b_***_Edward Miller
daa6762111f9ebf62b9c2ec655512783_***_Daniel Rees
891091891b6eee412668ae216f713312_***_Roderick Thomas
author Tinotenda Masawi
Edward Miller
Daniel Rees
Roderick Thomas
author2 Tinotenda Masawi
Edward Miller
Daniel Rees
Roderick Thomas
format Journal article
container_title Journal of Decision Systems
container_volume 34
container_issue 1
publishDate 2025
institution Swansea University
issn 1246-0125
2116-7052
doi_str_mv 10.1080/12460125.2025.2458874
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
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description The rapid advancement of Artificial Intelligence (AI) is transforming healthcare, offering both opportunities and challenges. This study examines the perceptions of healthcare practitioners in Wales regarding AI’s role in diagnostics. Through semi-structured interviews with 10 expert practitioners from various specializations, it uncovers diverse views shaped by experience and second-hand knowledge. While AI is recognized for enhancing diagnostic accuracy and administrative efficiency, concerns persist about the loss of human touch, data security, and biases. A key finding is the unanimous call for comprehensive AI training to bridge knowledge gaps and build confidence. Using an interpretivist qualitative approach, with purposive sampling and thematic analysis, the study highlights nuanced practitioner perspectives. The findings underscore the need for equitable AI resource distribution and tailored training to address geographical disparities. The study advocates for future research with larger, more diverse samples and follow-up evaluations to assess AI training’s long-term impact on healthcare practice.
published_date 2025-02-04T07:31:01Z
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