Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory

Singh, Nidhi; Jain, Monika; Kamal, Muhammad Mustafa; Bodhi, Rahul; Gupta, Bhumika

doi:10.1016/j.techfore.2023.122967

Cited by 24 publications

(2 citation statements)

References 49 publications

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“…This study draws on paradox theory (Ozanne et al ., 2016) to structure the research into the relationships between the utilization of generative AI and the sustainability performance of manufacturers within the framework of Industry 5.0. Paradox theory provides a lens to understand and manage the inherent tensions and contradictions that arise in the pursuit of sustainability goals through technological innovation (Singh et al ., 2024). By applying paradox theory, this research aims to explore how manufacturers can effectively leverage generative AI technologies to enhance sustainability performance while navigating the complex interplay of competing demands and objectives.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

Ghobakhloo,

Fathi,

Iranmanesh

et al. 2024

JMTM

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PurposeThis study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.Design/methodology/approachThe study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.FindingsGenerative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.Practical implicationsWhile each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.Originality/valueThis study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

Ghobakhloo,

Fathi,

Iranmanesh

et al. 2024

JMTM

View full text Add to dashboard Cite

show abstract

“…Furthermore, the trust and confidence of healthcare professionals in AI tools form the bedrock of their widespread adoption [8]. The degree to which ENT specialists trust and feel confident in utilizing AI-assisted diagnostics is a critical determinant of how these technologies are embraced and applied in patient care [9]. This trust is closely tied to their overall satisfaction with AI tools, as well as their perspectives on the future role of AI in ENT diagnostics [10].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Alshehri,

Alahmari,

Alasiry

2024

JPM

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The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants’ familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

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Literature Review on Human-Automation Interaction: Relationship Between Healthcare Automation and Human Error at Work

Cho,

Seo,

Duffy

2024

Lecture Notes in Computer Science

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Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory

Cited by 24 publications

References 49 publications

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Literature Review on Human-Automation Interaction: Relationship Between Healthcare Automation and Human Error at Work

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