Artificial intelligence-enabled non-intrusive vigilance assessment approach to reducing traffic controller’s human errors

2021 ◽  
pp. 108047
Author(s):  
Fan Li ◽  
Chun-Hsien Chen ◽  
Ching-Hung Lee ◽  
Shanshan Feng
Keyword(s):  
Artificial Intelligence ◽  
Human Errors ◽  
Assessment Approach

scholarly journals Rise of robots in surgical territory

2022 ◽  
Author(s):  
Mariyam S. ◽  
Haris P. ◽  
Sasi M. P. ◽  
Babu D. ◽  
Lakshmanan . ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Robotic Surgery ◽  
Human Errors ◽  
Operation Theatre

Robotic surgery is a rapid advancement in the scientific strata of artificial intelligence and has evolved into a refined tool for the surgeons. Over the last 30 years, this field has evolved in leaps and bounds with wide applications in the field of surgery by improving the dexterity and accessibility for the surgeons in various array of major complicated cases. The surgical armamentarium has been strengthened by evolution of robotic surgery to an extent that man may be replaced by artificial intelligence-based robots in the operation theatre, thereby eliminating the possibility of human errors and limitations.

Artificial intelligence in brachytherapy: a summary of recent developments

2021 ◽  
pp. 20200842
Author(s):  
Susovan Banerjee ◽  
Shikha Goyal ◽  
Saumyaranjan Mishra ◽  
Deepak Gupta ◽  
Shyam Singh Bisht ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
External Beam Radiotherapy ◽  
Intensity Modulated Radiotherapy ◽  
External Beam ◽  
Human Errors ◽  
Ample Evidence ◽  
Recent Developments ◽  
Human Effort ◽  
Almost All ◽  
Direct Use

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.

scholarly journals Detecting orientation of Brain MR scans using deep learning

2021 ◽  
Author(s):  
Chinmay Singhal ◽  
Nihit Gupta ◽  
Anouk Stein ◽  
Quan Zhou ◽  
Leon Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Free Text ◽  
Learning Models ◽  
Human Errors ◽  
Linguistic Barriers ◽  
Model Training ◽  
The Impact ◽  
Deep Learning Model ◽  
Made In

AbstractThere has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and orientation [1]. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable [2, 3, 4]. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.

scholarly journals Challenges for introducing artificial intelligence to improve the efficiency of a next generation assessment approach

eLearn ◽  
2021 ◽  
Vol 2021 (Special Issue) ◽  
pp. 1-15
Author(s):  
Brian Moon ◽  
Farina Beat ◽  
Sneha Nair ◽  
Andrew Slaughter
Keyword(s):  
Artificial Intelligence ◽  
Next Generation ◽  
Assessment Approach

The Human Error and Functional Failure Reasoning Framework: How Does It Scale?

2021 ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Human Error ◽  
Risk Assessments ◽  
Modular Approach ◽  
Human Errors ◽  
Functional Failure ◽  
Computational Costs ◽  
Complex Engineered Systems ◽  
Assessment Approach ◽  
Engineered Systems ◽  
And Performance

Abstract The goal of this research is to demonstrate the applicability of the Human Error and Functional Failure Reasoning (HEFFR) framework to complex engineered systems. Human errors are cited as a root cause of a majority of accidents and performance losses in complex engineered systems. However, a closer look would reveal that such mishaps are often caused by complex interactions between human fallibilities, component vulnerabilities, and poor design. Hence, there is a growing call for risk assessments to analyze human errors and component failures in combination. The HEFFR framework was developed to enable such combined risk assessments. Until now, this framework has only been applied to simple problems, and it is prone to be computationally heavy as complexity increases. In this research, we introduce a modular HEFFR assessment approach as means of managing the complexity and computational costs of the HEFFR simulations of complex engineered systems. Then, we validate the proposed approach by testing the consistency of the HEFFR results between modular and integral assessments and between different module partitioning assessments. Next, we perform a risk assessment of a train locomotive using the modular approach to demonstrate the applicability of the HEFFR framework to complex engineered systems. The results show that the proposed modular approach can produce consistent results while reducing complexity and computational costs. Also, the results from the train locomotive HEFFR analysis show that the modular assessments can be used to produce risk insights similar to integral assessments but with a modular context.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence

Recent Developments in the Assessment and Diagnosis of Child Psychopathology

1995 ◽  
Vol 11 (3) ◽  
pp. 203-212 ◽  
Author(s):  
Frank C. Verhulst
Keyword(s):  
Problem Behaviors ◽  
Child Psychopathology ◽  
Developmental Differences ◽  
Multiple Informants ◽  
Cultural Research ◽  
Assessment Approach ◽  
Multicultural Knowledge ◽  
Recent Developments ◽  
Basic Characteristics ◽  
Assessment And Diagnosis

In this article, recent developments in the assessment and diagnosis of child psychopathology are discussed with an emphasis on standardized methodologies that provide data that can be scored on empirically derived groupings of problems that tend to co-occur. Assessment methodologies are highlighted that especially take account of the following three basic characteristics of child psychopathology: (1) the quantitative nature of child psychopathology; (2) the role of developmental differences in the occurrence of problem behaviors, and (3) the need for multiple informants. Cross-cultural research is needed to test the applicability of assessment procedures across different settings as well as the generalizability of taxonomic constructs. Assessments of children in different cultures can be compared or pooled to arrive at a multicultural knowledge base which may be much stronger than knowledge based on only one culture. It is essential to avoid assuming that data from any single source reveal the significance of particular problems. Instead, comprehensive assessment of psychopathology requires coordination of multisource data using a multiaxial assessment approach.

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