scholarly journals 361 The Automation of Doctors and Machines: A Classification for AI in Medicine (ADAM framework)

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
F Kazzazi
Keyword(s):  
Artificial Intelligence ◽  
Medical Technology ◽  
Core Competencies ◽  
Modern Medicine ◽  
Current Medical ◽  
Levels Of Care ◽  
The Core ◽  
Normative Framework ◽  
Minimum Requirements

Abstract Aim The advances in Artificial Intelligence (AI) provide an opportunity to expand the frontier of medicine to improve diagnosis, efficiency, and management. By extension of being able to perform any task that a human could, a machine that meets the requirements of General AI (AGI) possesses the basic necessities to perform as, or at least qualify to become, a doctor. In this emerging field, this article explores the distinctions between doctors and AGI, and the prerequisites for AGI performing as clinicians. With its imminent arrival, it is beneficial to create a framework from which leading institutions can define specific criteria for AGI. Method A normative framework was derived from medical ethical literature and current medical technology. Comparisons were made between current capabilities and the traits of doctors ('doctorhood'). A framework was created that could fulfil current patient and doctor considerations for the use of AI in medicine. Results This Automation of Doctors and Machines (ADAM) framework is set out across 5 levels. As the level progresses, so do the minimum requirements in the core competencies of knowledge, safety, emotion, and independence. Conclusions The development of AI brings with it an exciting era of modern medicine. In order to fully enhance, expand, and regulate this field, the ADAM framework provides a tool to classify its use in medicine. In being able to categorize forms of medical AI, this allows clinicians, patients, and regulators to delineate different forms of AI, and a foundation is created from which governing bodies can set and standardise levels of care.

scholarly journals AI & CoE

2019 ◽  
Vol 9 (1) ◽  
pp. 7208-7212
Keyword(s):  
Artificial Intelligence ◽  
Research And Development ◽  
Core Competencies ◽  
Management Systems ◽  
Unique Combination ◽  
Substantial Impact ◽  
The Core ◽  
Strategic Resources ◽  
Centre Of Excellence ◽  
Organizational Problems

Artificial Intelligence or AI is being positioned as the panacea for all organizational problems; while Centre of Excellence or CoE, which is distinctly different from Research and Development activities helps organizations in their pursuit of higher revenue and profit. In this paper, the researchers have analysed the growth and importance of each of these two concepts – AI and the CoE; and have worked towards putting them together for creating a unique combination which shall benefit the organizations and hence the economy at large. In the process, a framework is provided for companies to improve, innovate, optimize, and eventually automate their management systems while making the core-competencies of their business AI proof. It is hoped that through this framework, organizations will be able to create a substantial impact by improving existing capabilities and actively creating new strategic resources in the interest of all the stakeholders.

scholarly journals Twenty-Five Years of Successful Application of Constraint Technologies at Siemens

AI Magazine ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 67-80 ◽  
Author(s):  
Andreas Falkner ◽  
Gerhard Friedrich ◽  
Alois Haselböck ◽  
Gottfried Schenner ◽  
Herwig Schreiner
Keyword(s):  
Artificial Intelligence ◽  
Small Businesses ◽  
Core Competencies ◽  
Business Success ◽  
Industrial Plants ◽  
Key Technology ◽  
High Productivity ◽  
The Core ◽  
Problem Solvers ◽  
Looking Back

The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. Because one of the core competencies of Siemens is to provide such highly engineered and customized systems, ranging from solutions for medium-sized and small businesses up to huge industrial plants, the efficient implementation and maintenance of configurators are important goals for the success of many departments. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period. In particular, we highlight the main technology factors regarding knowledge representation, reasoning, and integration which were important for our achievement. Finally we describe selected key application areas where the business success vitally depends on the high productivity of configuration processes.

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Heikki Mansikka ◽  
Don Harris ◽  
Kai Virtanen
Keyword(s):  
Path Analysis ◽  
Team Performance ◽  
Core Competencies ◽  
Core Competency ◽  
Principal Component ◽  
Analysis Model ◽  
Input Process ◽  
The Core ◽  
Performance Framework ◽  
Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

PROFESSIONAL COMPETENCIES OF ARCHITECTURE GRADUATES: PERCEPTIONS FROM GRADUATES, ACADEMICS AND EMPLOYERS IN THE NIGERIAN CONSTRUCTION INDUSTRY

2018 ◽  
Vol 15 (2) ◽  
pp. 1
Author(s):  
Joy Joshua Maina
Keyword(s):  
Best Practices ◽  
Construction Industry ◽  
Professional Practice ◽  
Core Competencies ◽  
Descriptive Statistics ◽  
Self Report ◽  
Professional Competencies ◽  
Architecture Education ◽  
The Core ◽  
Study Results

The clamour for better quality graduates by architects in the Nigerian Construction Industry (NCI) necessitates a look into the core competencies and the adequacy of architecture education in preparing architecture graduates for professional practice. 116 self-report likertscale questionnaires from architecture graduates (2009-2015), academics and employers were analysed to establish core competencies developed by the graduates while in school. Descriptive statistics, t-tests as well as Mann-Whitney tests for differences in ratings were employed for the study. Results reveal the perceived adequacy of architecture education for the future career of graduates from the academic perspective. Graduates were most proficient at design related competencies while AutoCAD was still considered the most important CAD competency for architecture graduates in the NCI. The study recommends more frequent evaluations of competencies for employability in collaboration with industry as well as embracing BIM related software in line with global best practices. Keywords: Academics, Architecture, Employers, Graduates, Professional competencies, NCI

Interdisciplinary Human Factors Curricula for Individualized, Independent Study

2020 ◽  
Vol 64 (1) ◽  
pp. 1390-1394
Author(s):  
Esa M. Rantanen ◽  
Hamza Khammash ◽  
James C. Hall
Keyword(s):  
Human Factors ◽  
Core Competencies ◽  
Panel Discussion ◽  
Independent Study ◽  
New Professionals ◽  
Interdisciplinary Programs ◽  
The Core ◽  
Disciplinary Programs ◽  
Board Of Certification ◽  
University Course

Education and career development of new generations of human factors professionals has rightly been a central concern the Human Factors and Ergonomics Society for many decades. There have been periodic surveys to track the changing employer expectations for new professionals, and there have been several panel discussion at the HFES Annual Meetings to address various issues in education of future professionals. There have been significant changes in academia, where many traditional disciplinary programs are declining and new interdisciplinary programs are emerging. These trends may present novel opportunities for education of the future human factors workforce. In this project we surveyed all courses in a university course catalog to identify courses that offer training, to varying degrees, in the Core Competencies as defined by the Board of Certification in Professional Ergonomics. These courses could form a basis for interdisciplinary programs in human factors without being confined in any particular department or existing program.

scholarly journals Research on machine learning Algorithm optimization based on 0-1 coding

2021 ◽  
Vol 2083 (4) ◽  
pp. 042086
Author(s):  
Yuqi Qin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Optimization ◽  
The Core ◽  
Coding Method ◽  
Discrete Manufacturing

Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

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