Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application

Hwang, Sewoong; Song, Yungyeong; Kim, Jonghyuk

doi:10.3390/app11062707

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…AI is applied to different contexts, so we can see applications to architecture in smart buildings and smart cities [6], smart mobility [9], medicine [10][11][12], smart industry (the 4th industrial revolution, as some authors [13] remark) and also smart education or smart classrooms [14].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Student Assessment: A Systematic Review

2021

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Artificial Intelligence (AI) is being implemented in more and more fields, including education. The main uses of AI in education are related to tutoring and assessment. This paper analyzes the use of AI for student assessment based on a systematic review. For this purpose, a search was carried out in two databases: Scopus and Web of Science. A total of 454 papers were found and, after analyzing them according to the PRISMA Statement, a total of 22 papers were selected. It is clear from the studies analyzed that, in most of them, the pedagogy underlying the educational action is not reflected. Similarly, formative evaluation seems to be the main use of AI. Another of the main functionalities of AI in assessment is for the automatic grading of students. Several studies analyze the differences between the use of AI and its non-use. We discuss the results and conclude the need for teacher training and further research to understand the possibilities of AI in educational assessment, mainly in other educational levels than higher education. Moreover, it is necessary to increase the wealth of research which focuses on educational aspects more than technical development around AI.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Student Assessment: A Systematic Review

2021

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“…Beyond AI and 3D applications, semantically segmented datasets are pivotal in telemedicine [53]. They facilitate real-time sharing and analysis, ensuring access to expert care regardless of location.…”

Section: Practical Use Of Segmented Radiological Datamentioning

confidence: 99%

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

Edelmers,

Kazoka,

Bolocko

et al. 2024

Diagnostics

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The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.

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“…In medicine, AI can support patient's care remotely by observing and comparing their medical information through a wearable device with sensors to other data that has already been collected using AI. This allows for constant monitoring of a patient and detecting changes that may be less obvious to humans (Hwang et al, 2021). AI in e-business is recommended and suggested by Malapane (2019) to be transformational by design and simplify processes.…”

Section: Ai In Organisationsmentioning

confidence: 99%

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

Okunlaya

Abdullah

Alias

2022

LHT

107

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PurposeArtificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery.Design/methodology/approachThis study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries.FindingsThis study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework.Research limitations/implicationsIn research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation.Practical implicationsIn practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution.Social implicationsThe enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community.Originality/valueAI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.

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Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application

Cited by 8 publications

References 44 publications

Artificial Intelligence for Student Assessment: A Systematic Review

Artificial Intelligence for Student Assessment: A Systematic Review

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

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