Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude

Ho, Tung Manh; Le, Ngoc-Thang B.; Ho, Manh-Toan; Mantello, Peter; Ghotbi, Nader

doi:10.1016/j.techsoc.2022.102166

Cited by 24 publications

(7 citation statements)

References 53 publications

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“…Concerning with artificial intelligence factors results indicate that trendiness had insignificant impact user satisfaction and hence rejecting argument developed by Chung, Ko [ 27 ]. Therefore, visual attractiveness has shown positive impact user satisfaction and consistent with prior studies Bhandari, Chang [ 30 ], Gupta, Gaurav [ 31 ], Ho, Le [ 32 ]. Moving further problem solving and communication quality have shown positive influence user satisfaction and in line with prior researchers findings [ 13 , 43 , 44 ].…”

Section: Discussionsupporting

confidence: 86%

Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking

Alnaser

Rahi

Alghizzawi

et al. 2023

Heliyon

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Section: Discussionsupporting

confidence: 86%

Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking

Alnaser

Rahi

Alghizzawi

et al. 2023

Heliyon

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“…Patient acceptance and healthcare provider training are essential for the successful adoption and implementation of AI-driven DR screening programs in rural settings (Farayola et al, 2023). Patients may have concerns or reservations about AI technologies, including distrust, fear of job displacement, or misunderstanding about the role of AI in healthcare decision-making (Ho et al, 2023). Effective communication, education, and engagement strategies are needed to build trust, address misconceptions, and promote acceptance of AI-based screening among patients, caregivers, and community members (Asan and Choudhury, 2021;Eboigbe et al, 2023).…”

Section: Challenges and Limitationsmentioning

confidence: 99%

Ai Applications in Screening and Diagnosis of Diabetic Retinopathy in Rural Settings

Rawlings Chidi,

Ugochukwu Odimba

2024

imsrj

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Diabetic retinopathy (DR) remains a significant cause of vision impairment and blindness, particularly in rural settings where access to specialized healthcare services is limited. The integration of artificial intelligence (AI) holds promise in revolutionizing the screening and diagnosis of DR, offering a scalable solution to bridge the gap in healthcare disparities. This systematic review synthesizes existing literature on AI applications tailored for screening and diagnosing diabetic retinopathy in rural areas. Through a comprehensive search across various databases, including PubMed, IEEE Xplore, and Google Scholar, a total of 88 studies meeting the inclusion criteria were identified. These studies encompassed a range of AI techniques, including deep learning algorithms, machine learning models, and image processing methods, deployed in diverse rural healthcare settings globally. The findings reveal that AI-based systems demonstrate high accuracy, sensitivity, and specificity in detecting diabetic retinopathy from fundus images, thereby enabling early identification and timely intervention. Moreover, the scalability and cost-effectiveness of these AI solutions make them particularly suitable for resource-constrained rural environments. However, several challenges persist, including the need for robust validation studies, integration with existing healthcare infrastructure, and addressing ethical and regulatory concerns. Additionally, considerations regarding data privacy, patient acceptance, and healthcare provider training are crucial for the successful implementation of AI-driven DR screening programs in rural settings. This systematic review underscores the transformative potential of AI technologies in improving access to diabetic retinopathy screening and diagnosis in rural areas. Future research should focus on addressing the identified challenges and optimizing AI systems to enhance their efficacy and accessibility in underserved communities. Keywords: AI, Rural, Diagnosis, Diabetic, Retinopathy, Rural, Review.

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“…35 In recent years, healthcare and business industries have been more eager to implement AI with emotions. 36,37 However, a crucial question remains: can AI learn without data? The nature of AI learning ability is based on data, which can be in any form.…”

Section: Conflict In Ai-himentioning

confidence: 99%

Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

Arunthavanathan,

Sajid,

Amin

et al. 2024

AIChE Journal

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The growth of artificial intelligence (AI) has allowed industries to automate and improve their efficiency in operations. Especially in process industries, AI helps to develop intelligent models and tools to proactively monitor and predict equipment or system failures, minimize downtime, and optimize maintenance schedules. With the advancements in AI and its ability to perform tasks, there is a growing belief that AI may eventually replace humans. However, the absence of human involvement in operations in the process industry raises safety concerns. Therefore, AI should collaborate with humans rather than replace them in processing facility operations. This technology is referred to as intelligence augmentation (IA). This article (i) presents a detailed comparison between AI and IA's potential in process systems, (ii) identifies the feasibility of using AI and IA in process safety, and (iii) identifies the risk associated with the implementation of AI or IA in process industries.

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Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude

Cited by 24 publications

References 53 publications

Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking

Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking

Ai Applications in Screening and Diagnosis of Diabetic Retinopathy in Rural Settings

Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

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