Acceptance, initial trust formation, and human biases in artificial intelligence: Focus on clinicians

Choudhury, Avishek; Elkefi, Safa

doi:10.3389/fdgth.2022.966174

Cited by 11 publications

(9 citation statements)

References 19 publications

(19 reference statements)

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“…In fact, most AI tools that have shown good performance during development are impractical in clinical practice,20 and according to a survey published on the BBC in 2020, 80% of healthcare AI apps fail to meet the National Health Service standards 21. Challenges at the individual level included issues around the awareness and trust of individuals on AI 16 22. In his research, Choudhury17 derived a framework that focuses on the interaction between AI and clinicians.…”

Section: Introductionmentioning

confidence: 99%

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Wu,

Xu,

Bai

et al. 2023

BMJ Open

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ObjectivesMedical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public’s views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public’s understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice.DesignThis was a meta-synthesis of qualitative studies.MethodA search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public’s perception of the application of AI in healthcare.ResultsOf the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public’s perspective, ethical and legal concerns about medical AI from the public’s perspective, and public suggestions on the application of AI in medical field.ConclusionResults showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public’s perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice.PROSPERO registration numberCRD42022315033.

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Section: Introductionmentioning

confidence: 99%

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Wu,

Xu,

Bai

et al. 2023

BMJ Open

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“…Usability and technology acceptance to ultimately bring solutions for clinicians into their daily routine: For clinicians, both usability and technology acceptance are critical factors to consider when evaluating and implementing new clinical procedures [ 71 ]. A technology that is easy to use and fits seamlessly into their workflow is more likely to be adopted and used effectively [ 72 ].…”

Section: What Else Could Be Done For An Improved Guideline Adherence ...mentioning

confidence: 99%

Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

Salybekov,

Wolfien,

Hahn

et al. 2024

Biomedicines

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The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.

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“…The formation of initial trust in technology is in uenced by a range of factors, including trusting foundations, beliefs, attitudes, the subjective norm within an organization, and trusting intentions (Choudhury and Elke , 2022;Li et al, 2008). Additionally, trust in technology is dependent on the existence of ethical governance, which entails ethical behaviors demonstrated by both technology designers and the organizations they are a liated with.…”

Section: Impact Of Ai Workload On Trust In Ai -Hypothesismentioning

confidence: 99%

Care providers’ perception of artificial intelligence: focus on workload, risk, trust, clinical decision-making, and clinical integration.

Shamszare,

Choudhury

2023

Preprint

Self Cite

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Despite the widespread use of AI systems in various industries, the sensitivity of human life presents a challenge for healthcare practitioners to realize the potential of AI in clinical settings fully. To address this challenge, this study investigated the factors contributing to the dynamic relationship between AI and clinicians using structural equation modeling. The considered factors were clinicians’ trust in AI, their perception of AI risk and workload, and its impact on clinical decision-making. The findings indicate that AI's ability to reduce workload positively impacts trust, while the perception of AI risk does not significantly impact trust. Also, the results showed that reduced workload and increased trust in AI positively impact the perception of AI-driven clinical decision-making. In contrast, a higher perception of AI risk is negatively related to the perception of AI-driven clinical decision-making. The findings of this study provided pathways for future research and recommendation on factors influencing AI utilization in clinical settings. The study also proposes a better way to integrate AI into clinical workflows that is more likely to improve trust in the technology and subsequent clinical decision-making.

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Acceptance, initial trust formation, and human biases in artificial intelligence: Focus on clinicians

Cited by 11 publications

References 19 publications

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

Care providers’ perception of artificial intelligence: focus on workload, risk, trust, clinical decision-making, and clinical integration.

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