Integrated Analytical Framework for the Development of Artificial Intelligence-Based Medical Devices

2021 ◽  
Author(s):  
Hirokazu Arima ◽  
Shingo Kano
Keyword(s):  
Artificial Intelligence ◽  
Medical Devices ◽  
Analytical Framework

scholarly journals Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3532 ◽  
Author(s):  
Ryuji Hamamoto ◽  
Kruthi Suvarna ◽  
Masayoshi Yamada ◽  
Kazuma Kobayashi ◽  
Norio Shinkai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Medical Devices ◽  
The United States ◽  
Medical Data ◽  
Clinical Implementation ◽  
Medical Field ◽  
Core Technology ◽  
Artificial Intelligence Technology ◽  
History Of

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

scholarly journals ASSESSING ETHICAL AI-BASED DECISION-MAKING: TOWARDS AN APPLIED ANALYTICAL FRAMEWORK

2019 ◽  
Vol 2019 ◽  
Author(s):  
Paul Henman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Analytical Framework ◽  
Government Accountability ◽  
Ethical Challenges ◽  
Government Decision ◽  
Policy Work ◽  
Key Questions

Globally there is strong enthusiasm for using Artificial Intelligence (AI) in government decision making, yet this technocratic approach is not without significant downsides including bias, exacerbating discrimination and inequalities, and reducing government accountability and transparency. A flurry of analytical and policy work has recently sought to identify principles, policies, regulations and institutions for enacting ethical AI. Yet, what is lacking is a practical framework and means by which AI can be assessed as un/ethical. This paper provides an overview of an applied analytical framework for assessing the ethics of AI. It notes that AI (or algorithmic) decision-making is an outcome of data, code, context and use. Using these four categories, the paper articulates key questions necessary to determine the potential ethical challenges of using an AI/algorithm in decision making, and provides the basis for their articulation within a practical toolkit that can be demonstrated against known AI decision-making tools.

scholarly journals Artificial Intelligence in Current Diabetes Management and Prediction

2021 ◽  
Vol 21 (12) ◽  
Author(s):  
Akihiro Nomura ◽  
Masahiro Noguchi ◽  
Mitsuhiro Kometani ◽  
Kenji Furukawa ◽  
Takashi Yoneda
Keyword(s):  
Artificial Intelligence ◽  
Medical Devices ◽  
Prediction Models ◽  
Diabetes Management ◽  
Predictive Performance ◽  
Management Tool ◽  
Dramatic Improvement ◽  
The Us ◽  
Diabetes Prediction ◽  
Computational Resources

Abstract Purpose of Review Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied by the dramatic improvement in computer performance. In this review, we introduce AI/ML-based medical devices and prediction models regarding diabetes. Recent Findings In the field of diabetes, several AI-/ML-based medical devices and regarding automatic retinal screening, clinical diagnosis support, and patient self-management tool have already been approved by the US Food and Drug Administration. As for new-onset diabetes prediction using ML methods, its performance is not superior to conventional risk stratification models that use statistical approaches so far. Summary Despite the current situation, it is expected that the predictive performance of AI will soon be maximized by a large amount of organized data and abundant computational resources, which will contribute to a dramatic improvement in the accuracy of disease prediction models for diabetes.

Artificial intelligence based on effectiveness of inverted classroom teaching of college sports

2020 ◽  
pp. 1-11
Author(s):  
Jianqin Cheng ◽  
Xiaomeng Wang
Keyword(s):  
Artificial Intelligence ◽  
Physical Education ◽  
Real Time ◽  
Visual Analysis ◽  
Analytical Framework ◽  
Colleges And Universities ◽  
Classroom Teaching ◽  
Teaching Behaviors ◽  
Inverted Classroom ◽  
Artificial Intelligence Technology

This study takes the effectiveness analysis of inverted classroom teaching in colleges and universities as a breakthrough point, and combines artificial intelligence technology with the analysis method of inverted classroom teaching in colleges and universities to enrich the existing methods for analyzing, the behavior of inverted classroom teaching in colleges and universities to realize the effectiveness of inverted classroom teaching in colleges and universities analysis. This research first constructs an analytical framework for the teaching behaviors of college physical education inverted classrooms based on artificial intelligence technology, which consists of observation dimension and the evaluation dimension. In order to further test the scientifically and operability of the analytical framework, taking emotion recognition as an example, practical operations are combined with specific examples to obtain visual analysis results. This study expands the dimension and depth of analysis of the behavior of inverted sport in classroom teaching in sport inversion colleges and universities, and has obvious advantages in saving manpower and real-time visual display. Through the analysis of the effectiveness of physical education inverted classroom teaching in sports inversion colleges and universities through artificial intelligence technology, the use of technology to participate in the analysis of physical education inverted classroom teaching behaviors in sports inverted colleges and universities, shorten the evaluation time, expand the evaluation dimension, improve the evaluation efficiency, achieve real-time feedback, real-time attention to classroom effects. Effectively regulating the inverted classroom teaching behavior of college physical education can promote the cultivation of teachers’ professional abilities, scientifically and accurately improve and correct teaching problems, and improve the quality of education and teaching. Eventually, students will achieve comprehensive self-evaluation of students, and promote personalized and standardized growth of students.

scholarly journals Malaysian Medical Device Regulation for Artificial Intelligence in Healthcare: Have all the pieces fallen into position?

2021 ◽  
Vol 6 (16) ◽  
pp. 137-144
Author(s):  
Nurus Sakinatul Fikriah Mohd Shith Putera ◽  
Sarah Munirah Abdullah ◽  
Noraiza Abdul Rahman ◽  
Rafizah Abu Hassan ◽  
Hartini Saripan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Device ◽  
Medical Devices ◽  
Publishing House ◽  
Legal Reasoning ◽  
Healthcare Applications ◽  
Artificial Intelligence And Law ◽  
Learning And Adaptation ◽  
Research Findings ◽  
Medical Device Regulation

Artificial Intelligence (AI) ability of self-learning and adaptation has challenged the medical device regulation in overseeing the safety and effectiveness of medical devices. Thus, this research aims to evaluate the adequacy of the pre-market requirements under the Medical Device Act 2012 in governing AI modification. Employing the doctrinal research methodology, systematic means of legal reasoning pertinent to AI for healthcare applications are produced. An effective medical device regulation is pivotal to foster trustworthiness in the governance and adoption of AI. However, the research findings indicate the deficiency of the current conformity assessment for medical devices in addressing AI modifications. Keywords: Artificial Intelligence and Law, Artificial Intelligence and Medical Device Regulation, Malaysian Medical Device Regulation eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i16.2635

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