Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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Artificial Intelligence in radiotherapy: state of the art and future directions

Medical Oncology ◽

10.1007/s12032-020-01374-w ◽

2020 ◽

Vol 37 (6) ◽

Author(s):

Giulio Francolini ◽

Isacco Desideri ◽

Giulia Stocchi ◽

Viola Salvestrini ◽

Lucia Pia Ciccone ◽

...

Keyword(s):

Artificial Intelligence ◽

State Of The Art ◽

Future Directions

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Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

ACM SIGSOFT Software Engineering Notes ◽

10.1145/3448992.3448999 ◽

2021 ◽

Vol 46 (2) ◽

pp. 28-29

Author(s):

Benoît Vanderose ◽

Julie Henry ◽

Benoît Frénay ◽

Xavier Devroey

Keyword(s):

Artificial Intelligence ◽

Software Engineering ◽

Engineering Education ◽

State Of The Art ◽

Future Directions ◽

The Past ◽

Software Engineering Education ◽

Current State ◽

Amount Of Knowledge ◽

Software Engineering Tools

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

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Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

E3S Web of Conferences ◽

10.1051/e3sconf/202129701074 ◽

2021 ◽

Vol 297 ◽

pp. 01074

Author(s):

Achsha Babu ◽

J. Andrew Onesimu ◽

K. Martin Sagayam

Keyword(s):

Artificial Intelligence ◽

State Of The Art ◽

Future Directions ◽

Human Essence ◽

Research Challenges ◽

Different Types ◽

Precise Diagnosis ◽

Clinical Practise ◽

To Come ◽

Applications Of Ai

Artificial Intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the healthcare industry. In the domains of dentistry and medicine, artificial intelligence has yet to come a long way. As a result, dentists must be aware of the potential implications for a profitable clinical practise in the future. In this paper, we present the current applications of AI in dentistry. The different types of AI techniques are introduced and summarized. The state-of-the-art literature is studied analysed. A comparative analysis on the different AI techniques in dentistry is presented. Further, the research challenges in the field of dentistry and future directions are also provided.

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Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031117 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1117

Author(s):

Tarik Alafif ◽

Abdul Muneeim Tehame ◽

Saleh Bajaba ◽

Ahmed Barnawi ◽

Saad Zia

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

State Of The Art ◽

Diagnosis And Treatment ◽

Future Directions ◽

Health Community ◽

The World ◽

The Status ◽

And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

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Artificial Intelligence Applications in Otology: A State of the Art Review

Otolaryngology ◽

10.1177/0194599820931804 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1123-1133 ◽

Cited By ~ 1

Author(s):

Eunice You ◽

Vincent Lin ◽

Tamara Mijovic ◽

Antoine Eskander ◽

Matthew G. Crowson

Keyword(s):

Artificial Intelligence ◽

Full Text ◽

State Of The Art ◽

Directed Search ◽

Future Directions ◽

Key Stakeholders ◽

Full Text Retrieval ◽

Processing Techniques ◽

Text Language ◽

Applications Of Ai

Objective Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions. Data Sources Two comprehensive databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles that applied AI to otology. Review Methods An initial abstract and title screening was completed. Exclusion criteria included nonavailable abstract and full text, language, and nonrelevance. References of included studies and relevant review articles were cross-checked to identify additional studies. Conclusion The database search identified 1374 articles. Abstract and title screening resulted in full-text retrieval of 96 articles. A total of N = 38 articles were retained. Applications of AI technologies involved the optimization of hearing aid technology (n = 5; 13% of all articles), speech enhancement technologies (n = 4; 11%), diagnosis and management of vestibular disorders (n = 11; 29%), prediction of sensorineural hearing loss outcomes (n = 9; 24%), interpretation of automatic brainstem responses (n = 5; 13%), and imaging modalities and image-processing techniques (n = 4; 10%). Publication counts of the included articles from each decade demonstrated a marked increase in interest in AI in recent years. Implications for Practice This review highlights several applications of AI that otologists and otolaryngologists alike should be aware of given the possibility of implementation in mainstream clinical practice. Although there remain significant ethical and regulatory challenges, AI powered systems offer great potential to shape how healthcare systems of the future operate and clinicians are key stakeholders in this process.

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Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000240 ◽

2016 ◽

Vol 224 (2) ◽

pp. 62-70 ◽

Cited By ~ 2

Author(s):

Thomas Straube

Keyword(s):

Anxiety Disorders ◽

Neuronal Plasticity ◽

Functional Neuroimaging ◽

State Of The Art ◽

Methodological Issues ◽

Future Directions ◽

Activation Patterns ◽

New Learning ◽

Analytical Strategies ◽

Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

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