scholarly journals Are Artificial Intelligence (AI) And Machine Learning (ML) Having An Effective Role In Helping Humanity Address The New Coronavirus Pandemic?

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Outbreaks ◽  
Hospital Capacity ◽  
Data Engineering ◽  
Clinical Presentations ◽  
The Face ◽  
Effective Role ◽  
Using Data ◽  
The Right

COVID-19 has become a pandemic affecting the most of countries in the world. One of the most difficult decisions doctors face during the Covid-19 epidemic is determining which patients will stay in hospital, and which are safe to recover at home. In the face of overcrowded hospital capacity and an entirely new disease with little data-based evidence for diagnosis and treatment, the old rules for determining which patients should be admitted have proven ineffective. But machine learning can help make the right decision early, save lives and lower healthcare costs. So, there is therefore an urgent and imperative need to collect data describing clinical presentations, risks, epidemiology and outcomes. On the other side, artificial intelligence(AI) and machine learning(ML) are considered a strong firewall against outbreaks of diseases and epidemics due to its ability to quickly detect, examine and diagnose these diseases and epidemics.AI is being used as a tool to support the fight against the epidemic that swept the entire world since the beginning of 2020.. This paper presents the potential for using data engineering, ML and AI to confront the Coronavirus, predict the evolution of disease outbreaks, and conduct research in order to develop a vaccine or effective treatment that protects humanity from these deadly diseases.

scholarly journals Mejoramiento vegetal moderno, inteligencia artificial y derechos de propiedad intelectual

2020 ◽  
Vol 1 (2) ◽  
pp. 839-866
Author(s):  
Miguel A. Rapela
Keyword(s):  
Artificial Intelligence ◽  
Plant Breeding ◽  
Intellectual Property Rights ◽  
Human Person ◽  
Living Matter ◽  
Plant Variety ◽  
The Face ◽  
The One ◽  
The Right ◽  
Biotechnological Inventions

The modern plant breeding to obtain new plant varieties is based on genomic and phenomic selection generated through big data with millions of information points. In the face of such a quantity of data, it is necessary to use artificial intelligence to combine a complete vision and analysis of the problem through a human-computer interaction never addressed.The use of artificial intelligence has already created interpretive challenges in patents and copyrights. To a greater extent, modern plant breeding with the assistance of artificial inte-lligence is exposing major disarticulations and anachronisms in the Plant Breeder’s Rights and patent systems for biotechnological inventions. The challenges may even extend to the question of who would be entitled to the right in the case of products obtained without human intervention.The analysis of the situation indicates, on the one hand, that it would be necessary a review of the international framework of intellectual property rights in plant living matter which is based on independent treaties and conventions that apply to an indivisible organism as is a new plant variety. A more logical proposal would be to have a single, modern, and up-to-date compre-hensive sui generis protection system for all types of plant germplasm. On the other hand, it is proposed that, even in the case of products obtained through complete artificial intelligence processes, there must always be a human person legally responsible of the consequences of their actions, whether positive or negative

Criticalities and Advantages of the Use of Artificial Intelligence in Research

2022 ◽  
pp. 161-175
Author(s):  
Jessica Camargo Molano ◽  
Jacopo Cavalaglio Camargo Molano
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Public Schools ◽  
School System ◽  
Detection System ◽  
Rapid Development ◽  
Academic Research ◽  
The Face ◽  
The Impact ◽  
Machine Learning Models

In recent years, artficial intelligence, through the rapid development of machine learning and deep learning, has started to be used in different sectors, even in academic research. The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on artificial intelligence. To understand what errors artificial intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system, and the “writing” of the first scientific text by artificial intelligence. In particular, this work takes into consideration the systematic errors due to the polarization of data with which the machine learning models are trained, the absence of feedback and the problem of minorities who cannot be represented through the use of big data.

Artificial intelligence in periodontics – A dip in the future

2021 ◽  
pp. 1-6
Author(s):  
Shivani Sachdeva ◽  
Amit Mani ◽  
Hiral Vora ◽  
Harish Saluja ◽  
Shubhangi Mani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Computer Software ◽  
Learning Content ◽  
The Face ◽  
Cad Cam ◽  
Radiographic Data ◽  
Cam Systems

BACKGROUND: Artificial intelligence is a relatively newer technology in the field of medical world. This science uses the machine – learning algorithm and computer software to aid in the diagnostics in medical and dental fields. It is a huge talking point in the field of technology which is spreading it’s wings in all possible sectors at a great speed. This field covers solutions from coaching solutions to diagnostics in medical field covering under the umbrella of all what can be achieved by machine and deep learning. CONTENT: In dentistry, artificial intelligence is creating a revolution in all sections from collection of data, creating algorithms for orthodontic procedures, diagnostic records in the aspect of radiographic data, three dimensional scans and cone beam computed tomography, CAD CAM systems for restorative and prosthetic purposes. Similarly continuous research is being done in the field of periodontics in terms of measuring bone loss, amount of plaque present and much more. CONCLUSION: The field of artificial technology with its varied applications will change the face of dentistry in the upcoming times. Artificial intelligence with its application of machine learning will change the face of dentistry in future.

scholarly journals Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Masateru Taniguchi ◽  
Shohei Minami ◽  
Chikako Ono ◽  
Rina Hamajima ◽  
Ayumi Morimura ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Speed ◽  
Simple Procedure ◽  
Rna Extraction ◽  
Disease Outbreaks ◽  
High Sensitivity ◽  
Cost Effective ◽  
Rt Pcr ◽  
Emerging Viruses

AbstractHigh-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Artificial intelligence--- Future of Anesthesiology!!

2019 ◽  
pp. 247-249
Author(s):  
Tariq H. Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care ◽  
Routine Practice ◽  
Surgical Practice ◽  
Anesthesia Practice ◽  
The Face ◽  
To Come ◽  
The Many ◽  
Applications Of Ai

The term artificial intelligence (AI) was introduced in 1950. There have been many attempts to develop machines capable of performing cognitive and skill based tasks of anesthesiologist based on the principles of AI. These attempts have not been successful because of the complexities of anesthesia practice. Recent innovations in AI, especially machine learning, will continue to grow in importance in the years to come and will greatly revolutionize the face of anesthesia along with surgical practice, perioperative medicine practiced in clinics, and imaging interpretation. Anesthesiologists should continue to embrace this technology, stay up to date with the advances in AI, and also make genuine efforts to smoothly assimilate it in their routine practice now so that they can be the revolutionaries of their own future. We hope to see an ever-widening spectrum of the uses of AI in all fields of medical practice, and anesthesiology is not an exception. Its time our friends start visualizing the many applications of AI in their practice. Citation: Khan FH, Fazal M. Artificial intelligence--- Future of Anesthesiology!! Anaesth pain & intensive care 2019;23(3):247-249

scholarly journals Machine learning spots the time to treat Huntington disease

2021 ◽  
Author(s):  
Igor Koval ◽  
Thomas Dighiero-Brecht ◽  
Allan Tobin ◽  
Sarah Tabrizi ◽  
Rachael Scahill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Outcome Measures ◽  
Huntington Disease ◽  
Disease Stage ◽  
New Approach ◽  
Clinical Biomarkers ◽  
Using Data ◽  
The Right ◽  
New Treatments ◽  
Disease Stages

Abstract We propose a new approach, based on machine learning, to evaluating new treatments for neurodegenerative diseases. Using data from two longitudinal studies of 299 participants with early Huntington Disease, we learned the range of likely trajectories of 15 imaging and clinical biomarkers from the premanifest to manifest disease stages. We positioned independent 11,510 patients on these maps using their baseline data and hence forecast the values of their biomarkers at any timepoint. Applied to trial design, we showed that sample size can be decreased by up to 50% by selecting individuals for whom we predict a significant change for their outcome measures during trial. Reduction occurs whatever the selected outcome measures and the targeted disease stage. This approach does not only select the right patient at the right time for the right trial, but also guides decisions about when to start preventive treatments.

scholarly journals Research of approaches and methods of applying artificial intelligence and machine learning in socio-economic processes

2020 ◽  
Vol 18 (2) ◽  
pp. 65-79
Author(s):  
Darya Yu. Sakhanevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Foundation ◽  
Social Economic ◽  
Historical Period ◽  
Model Data ◽  
Machine Learning Methods ◽  
The Right ◽  
One Machine ◽  
Statistical Indicators

One of the problems hindering the development of the socio-economic sphere in the innovative direction is the lack of structuring of approaches and methods used in machine learning as part of the introduction of artificial intelligence (AI) in socio-economic processes. The same problem hinders the growth of the pace of innovative development and, as a result, the improvement of the scientific and technical level of the country. The article classifies and systematizes aspects of machine learning, focuses on the need to accelerate the construction and implementation of algorithms as the basis of AI for increasing the efficiency of managing socio-economic processes. To achieve this goal, the following results are presented: analysis of the concepts of machine learning and AI, study of analytical materials regarding approaches and methods to the introduction of artificial intelligence and prospects for its application in socio-economic processes. There were systematized approaches to machine learning introduction to artificial intelligence depending on the historical period, the implementation of AI, and another, and methods according to the method of machine learning, predictive model data for creating AI algorithms (e.g., probabilistic), and the idea or the nature of the research that uses this technology (assessment and collection of statistical indicators, analysis). The study of the material related to machine learning and AI construction allowed us to draw the following conclusions. The theoretical foundation in the form of mathematical and statistical methods as the basis for building algorithms for creating AI in the framework of machine learning is a necessary part of the process of teaching computers human qualities. However, information about machine learning methods and approaches is mostly scattered, and it is necessary to form a unified methodological base in order to simplify the stage of searching for the right method of creating AI to solve any social, economic or other problem. The presence of such a database will create opportunities to replace one machine learning method for creating AI with another in different fields of activity and socio-economic processes.

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