Policy based generic autonomic adapter for a context-aware social-collaborative system

Hussain, Nazmul; Wang, Hai H.; Buckingham, Christopher D.

doi:10.1109/isacv.2018.8354044

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…For example, Cai and colleagues [38] interviewed 21 pathologists before, during, and after being presented the AI prediction results for prostate cancer diagnosis, to learn the types of information that they desired from the AI assistant. Two papers performed case studies [45] [34], while one study each conducted a field study [47] and survey [54]. Overall, we see the potential for conducting more field studies and surveys to gain wider understanding, as well as case studies to obtain more in-depth insights.…”

Section: Distribution Over Research Methodsmentioning

confidence: 99%

“…Particularly, 1 study investigated the collaboration between a robot and the whole surgical team in the operation room [46]. Eight papers focused on patient-AI collaboration targeting different diseases, e.g., workrelated musculoskeletal disorders [40], mental illness [45] and Parkinson's disease [37]. Six papers developed solutions dedicated to vulnerable populations, e.g., children and the elderly who need specific care, with 3 for children [39] Finally, we found a lack of theory-driven papers in the review.…”

Section: Themes Of Collaborationmentioning

confidence: 99%

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Human-AI Collaboration in Healthcare: A Review and Research Agenda

Lai¹,

Kankanhalli²,

Ong³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Advances in Artificial Intelligence (AI) have led to the rise of human-AI collaboration. In healthcare, such collaboration could mitigate the shortage of qualified healthcare workers, assist overworked medical professionals, and improve the quality of healthcare. However, many challenges remain, such as investigating biases in clinical decision-making, the lack of trust in AI and adoption issues. While there is a growing number of studies on the topic, they are in disparate fields, and we lack a summary understanding of this research. To address this issue, this study conducts a literature review to examine prior research, identify gaps, and propose future research directions. Our findings indicate that there are limited studies about the evolving and interactive collaboration process in healthcare, the complementarity of humans and AI, the adoption and perception of AI, and the longterm impact on individuals and healthcare organizations. Additionally, more theory-driven research is needed to inform the design, implementation, and use of collaborative AI for healthcare and to realize its benefits.

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Section: Distribution Over Research Methodsmentioning

confidence: 99%

Section: Themes Of Collaborationmentioning

confidence: 99%

Human-AI Collaboration in Healthcare: A Review and Research Agenda

Lai¹,

Kankanhalli²,

Ong³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Through the backing of AI, types of machinery have turned out to be cleverer and have generated a method to advance important benefits. The risks can be attained as soon as AI starts to develop pieces of machinery that are more intelligent than human beings [12].…”

Section: Application Of Artificial Intelligence (Ai) In Job Assessmen...mentioning

confidence: 99%

Intelligent-Based Job Applicants’ Assessment and Recruitment System

B. I.,

A. A.,

2024

British Journal of Computer, Networking and Information Technol

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In recent years, companies faced the challenge of sorting resumes from job applicants, assessing, recruiting, and notifying applicants manually. This study is centered on creating a system that will automate this process and resolve the problems associated with manual recruitment processes. The waterfall model was employed in the analysis and design of the system in this study due to its simplicity and sequential nature. The system was built using PHP programming language and the database was designed using MySQL. In this study, an intelligent-based job applicants’ assessment and recruitment system was developed using artificial intelligence techniques. It brings phenomenal success to Human Resource Management in a very short time. The recruitment process, in general, would experience a foremost modification, delivering rapid, efficient, and cost-effective methods of discovering prospective workers. Numerous opportunities were identified when utilizing this technology in recruitment, which include speeding up the staffing procedure, computerization of responsibilities, and rising detachment. Results obtained during the evaluation display that this method enhances the correctness of toning the right applicants with the right jobs. However, this system is still open to upgrading in the future for any researcher who finds this to be of interest.

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Policy based generic autonomic adapter for a context-aware social-collaborative system

Cited by 2 publications

References 26 publications

Human-AI Collaboration in Healthcare: A Review and Research Agenda

Human-AI Collaboration in Healthcare: A Review and Research Agenda

Intelligent-Based Job Applicants’ Assessment and Recruitment System

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