Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study

Abdullah, R.; Fakieh, Bahjat

doi:10.2196/17620

Cited by 140 publications

(127 citation statements)

References 16 publications

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“…Internationally, the empirical evidence to date has shown a mixed response on the part of healthcare professionals to AI innovations. Although they are seen to be largely positive about using AI tools [ 16 , 17 ], they are also concerned about job insecurity resulting from AI implementation [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

Chen

Stavropoulou

Narasinkan

et al. 2021

BMC Health Serv Res

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Background Artificial Intelligence (AI) innovations in radiology offer a potential solution to the increasing demand for imaging tests and the ongoing workforce crisis. Crucial to their adoption is the involvement of different professional groups, namely radiologists and radiographers, who work interdependently but whose perceptions and responses towards AI may differ. We aim to explore the knowledge, awareness and attitudes towards AI amongst professional groups in radiology, and to analyse the implications for the future adoption of these technologies into practice. Methods We conducted 18 semi-structured interviews with 12 radiologists and 6 radiographers from four breast units in National Health Services (NHS) organisations and one focus group with 8 radiographers from a fifth NHS breast unit, between 2018 and 2020. Results We found that radiographers and radiologists vary with respect to their awareness and knowledge around AI. Through their professional networks, conference attendance, and contacts with industry developers, radiologists receive more information and acquire more knowledge of the potential applications of AI. Radiographers instead rely more on localized personal networks for information. Our results also show that although both groups believe AI innovations offer a potential solution to workforce shortages, they differ significantly regarding the impact they believe it will have on their professional roles. Radiologists believe AI has the potential to take on more repetitive tasks and allow them to focus on more interesting and challenging work. They are less concerned that AI technology might constrain their professional role and autonomy. Radiographers showed greater concern about the potential impact that AI technology could have on their roles and skills development. They were less confident of their ability to respond positively to the potential risks and opportunities posed by AI technology. Conclusions In summary, our findings suggest that professional responses to AI are linked to existing work roles, but are also mediated by differences in knowledge and attitudes attributable to inter-professional differences in status and identity. These findings question broad-brush assertions about the future deskilling impact of AI which neglect the need for AI innovations in healthcare to be integrated into existing work processes subject to high levels of professional autonomy.

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Section: Introductionmentioning

confidence: 99%

Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

Chen

Stavropoulou

Narasinkan

et al. 2021

BMC Health Serv Res

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“…Several studies have focused on healthcare professionals' perception and attitude towards AI [15][16][17][18][19][20][21]. In medical physics, the only study on the perceptions, practices, and education needs about AI focused on European medical physics experts and their needs [10].…”

Section: Introductionmentioning

confidence: 99%

The perceptions of medical physicists towards relevance and impact of artificial intelligence

et al. 2021

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Arti cial intelligence (AI) is an innovative tool that is revolutionising healthcare and medical physics, possibly impacting clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this eld. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practice, the possibility of AI threatening/disrupting the medical physicists' practice and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 responders from 94 countries participated. More than 85% of the responders agree that AI will play an essential role in medical physicists' practice. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control, treatment planning will be performed by AI. Half of them thought AI would not threaten/disrupt the medical physicists' practice. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) admitted that they have no skill in AI. The general perception of medical physicists is that AI is here to stay, and it will in uence our practice. Medical physicists should be prepared with education and training for this new reality.

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“…However, AI-based drug development may be slower to launch in the long term. If applied correctly, these methods can be highly competitive in the pharmaceutical industry [ 29 , 30 ].…”

Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

128

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Background Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. Objective The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. Methods We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. Results Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. Conclusions We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.

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Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study

Cited by 140 publications

References 16 publications

Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

The perceptions of medical physicists towards relevance and impact of artificial intelligence

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

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