Knowledge, Attitudes and Perspectives of Anesthesiologists on Artificial Intelligence

Tasdogan, Ali Muhittin

doi:10.14744/ejmi.2020.54709

Cited by 4 publications

(9 citation statements)

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“…Employee perceptions and attitudes toward technological change continues to be studied within the manufacturing and automotive sectors which were among the first to automate ( Vieitez et al, 2001 ; Kim and Kim, 2018 ). However, recent developments in new technologies such as AI, robotics, and cloud computing are increasingly disrupting a large variety of sectors, including health care ( Blease et al, 2018 ; Doraiswamy et al, 2018 ; Sarwar et al, 2019 ; Tasdogan, 2020 ), wholesale and service sectors ( Hettich, 2017 ; Li et al, 2018 ; Meske and Junglas, 2020 ), banking/financial services and education ( Niedzwiecka and Pan, 2017 ; Bond et al, 2018 ; Mercader and Gairín, 2020 ).…”

Section: Review Findingsmentioning

confidence: 99%

“…In healthcare settings, recent surveys have found medical physicians to be both skeptical and optimistic about new digital technologies, such as AI ( Blease et al, 2018 ; Doraiswamy et al, 2018 ; Sarwar et al, 2019 ; Tasdogan, 2020 ). On the whole, physicians were not overly concerned about their jobs becoming obsolete and were doubtful about the potential of technology to outperform humans and replace human clinicians in delivering care ( Blease et al, 2018 ; Doraiswamy et al, 2018 ; Tasdogan, 2020 ).…”

Section: Review Findingsmentioning

confidence: 99%

“…. (2018);Bond et al (2018); Haar (2017, 2018); Cascio and Montealegre (2016)*;Cadwallader et al (2010); DiPietro et al (2014);Doraiswamy et al (2018);Hettich (2017);Li et al (2018);Li et al (2019);Mercader and Gairín (2020);Meske and Junglas (2020);Niedzwiecka and Pan (2017);Sarwar et al (2019);Schraeder et al (2006);Tasdogan (2020);Vieitez et al (2001) Skills and trainingBakker et al (2012); Beer and Mulder (2020)*;Berg and Chyung (2008); Blume et al (2010)*; Bolívar-Ramos et al (2012); Bode and Gold (2018); Börner et al (2018); Brown and Souto-Otero (2020); Brunetti et al (2020); Cascio (2019)*; Chauhan et al (2016); Ederer et al (2015); Gamrat et al (2014); Gorlitz and Tamm (2016); Grundke et al (2018); Harteis and Goller (2014); Li and Herd (2017); Martín-Rojas et al (2019); Melián-González and Bulchand-Gidumal (2017); Mercader and Gairín (2020); Noe et al (2014)*; Oberlander et al (2020)*; Osmundsen (2020); Sousa and Rocha (2019)* Workplace resilience and adaptability Baard et al (2014)*; Badran and Youssef-Morgan (2015); Burns et al (2013); Britt et al (2016)*; Cameron and Brownie (2010); Cullen et al (2014); Ferris et al (2005); Fisher et al (2018)*; Förster and Duchek (2017); Guo et al (2017); Harms et al (2017); Hartmann et al (2020)*; Huang et al (2014)*; Jensen et al (2008); Jung and Yoon (2015); Kinman and Grant (2011); Kossek and Perrigino (2016); Lamb and Cogan (2016); Larson and Luthans (2006); Lounsbury et al (2003); Luthans et al (2005); Luthans et al (2007); Luthar et al (2000); Lyons et al (2015); Mache et al (2014); Malik and Garg (2017); McDonald et al (2016); Ployhart and Bliese (2006); Seligman and Csikszentmihalyi, 2000; Stevenson et al (2011); Wanberg and Banas (2000); Wei and Taormina (2014); Welbourne et al (2015); Yang and Danes (2015); Youssef and Luthans (2007). Work-related stress and wellbeing Ayyagari et al (2011); Bakker and Demerouti (2007)*; Bakker and Demerouti (2017)*; Bouckenooghe et al (2013); Bowling et al (2010)*; Diener et al (2018)*; Edmans (2012); Field and Chan (2018); Fisher (2003); Jena (2015); Kazmi et al (2008); Koys (2001); Harter et al (2002)*; Judge et al (2001)*; Kagan (2016); Kinicki et al (2002)*; Krekel et al (2019)*; Lepine et al (2005)*; Liu et al (2019); Nisafani et al (2020)*; Ragu-Nathan et al (2008); Riketta (2008)*;Schneider et al (2003); Silvestro (2002); Tarafdar et al (2010); Tarafdar et al (2019)*; Tarafdar et al (2015); Tenney et al (2015); Tenney et al (2016)*; Wright et al (2002); Wright et al (2007); Zeike et al (2019a); Zeike et al (2019b) Group Team communication and collaboration Alshawi and Ingirige (2003)*; Anders (2016); Banker et al (2006); Berghaus and Back (2016); Bolstad and Endsley (2003)*; Boughzala et al, 2012; Boughzala and de Vreede (2015); Ellison et al (2014); Faems et al (2005); Fletcher and Major (2006); Gibson...…”

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confidence: 99%

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Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors

et al. 2021

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The rapid advancement of new digital technologies, such as smart technology, artificial intelligence (AI) and automation, robotics, cloud computing, and the Internet of Things (IoT), is fundamentally changing the nature of work and increasing concerns about the future of jobs and organizations. To keep pace with rapid disruption, companies need to update and transform business models to remain competitive. Meanwhile, the growth of advanced technologies is changing the types of skills and competencies needed in the workplace and demanded a shift in mindset among individuals, teams and organizations. The recent COVID-19 pandemic has accelerated digitalization trends, while heightening the importance of employee resilience and well-being in adapting to widespread job and technological disruption. Although digital transformation is a new and urgent imperative, there is a long trajectory of rigorous research that can readily be applied to grasp these emerging trends. Recent studies and reviews of digital transformation have primarily focused on the business and strategic levels, with only modest integration of employee-related factors. Our review article seeks to fill these critical gaps by identifying and consolidating key factors important for an organization’s overarching digital transformation. We reviewed studies across multiple disciplines and integrated the findings into a multi-level framework. At the individual level, we propose five overarching factors related to effective digital transformation among employees: technology adoption; perceptions and attitudes toward technological change; skills and training; workplace resilience and adaptability, and work-related wellbeing. At the group-level, we identified three factors necessary for digital transformation: team communication and collaboration; workplace relationships and team identification, and team adaptability and resilience. Finally, at the organizational-level, we proposed three factors for digital transformation: leadership; human resources, and organizational culture/climate. Our review of the literature confirms that multi-level factors are important when planning for and embarking on digital transformation, thereby providing a framework for future research and practice.

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Section: Review Findingsmentioning

confidence: 99%

Section: Review Findingsmentioning

confidence: 99%

mentioning

confidence: 99%

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Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors

et al. 2021

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“…The majority of AI experts think that the developments of this technology will increase the effectiveness of healthcare in the medical field and diagnostic accuracy and will reduce the workload of physicians [6][7][8]. In recent years, increasing access to health data and the rapid development of big data analysis methods have allowed successful AI applications in every area of medicine [9]. However, the debate is ongoing in the literature about the use of AI in medicine and particularly in the field of radiology.…”

Section: Discussionmentioning

confidence: 99%

“…In the same study, only 17.6% of the participants reported that AI may replace themselves [16]. Again in a survey conducted among psychiatrists in the Duke University and Harvard Medical Faculty, only 4% of the participants stated that AI may replace themselves [9]. In a survey study by Oh et al,35.4% of Korean physicians thought that AI would replace physicians [15].…”

Section: Open Endedmentioning

confidence: 91%

Evaluation of knowledge and attitudes of radiology department workers about artificial intelligence

Yurdaisik,

Aksoy

2021

Ann Clin Anal Med

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Aim: The objective of this study was to investigate the knowledge and attitudes of radiology department workers through a survey. Material and Methods: In this study, the opinions and attitudes of physicians, residents and technicians working in radiology departments of various hospitals and medical students in our university about AI were collected through a survey conducted between 01/07/2020 and 12/08/2020 and analyzed through a survey. Results: The response rate of the survey was 25.3%; 47.1% of the participants reported that they have enough knowledge about AI applications in general, while only 25% stated that they hadsufficient knowledge about AI applications in radiology. Among all participants, 35.3% thought that AI applications would negatively affect the profession of radiologists, while 30.3% thought that these applications would have positive impacts; 51.9% of the participants think that AI applications will save time for radiologists. The rate of participants who were concerned about the advances in AI was 16.1%. Discussion: Further similar studies should be conducted on this issue in order to obtain more detailed information and contribute to the literature. We believe that our results will be guiding for further comprehensive studies on the opinions of radiology department workers about the use of AI in the field of radiology.

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Artificial intelligence and nonoperating room anesthesia

Pardo,

Le Cam,

Verdonk

2024

Current Opinion in Anaesthesiology

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Purpose of review The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. Recent findings AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. Summary The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

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Knowledge, Attitudes and Perspectives of Anesthesiologists on Artificial Intelligence

Cited by 4 publications

References 0 publications

Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors

Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors

Evaluation of knowledge and attitudes of radiology department workers about artificial intelligence

Artificial intelligence and nonoperating room anesthesia

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