Artificial intelligence (AI) and machine learning are playing a significant role in understanding and addressing the crisis caused by COVID-19. The technology mimic human intelligence and ingest great volumes of data to quickly chart patterns and identify insights.
One example is when BenevolentAI, a global leader in the development and application of artificial intelligence for drug discovery, took just few days to find that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) is a strongest candidate and can be a potential treatment for COVID-19 patients.
This accelerated the clinical trials of #Baricitinib and Eli Lilly (a giant American Pharmaceutical company) has already commenced phase III clinical trials of Baricitinib to treat COVID-19.
Few more names include Deepmind, ImmunoPrecise, Insilico, healx, Imperial College, Tech Mahindra, and Deargen. Some Indian companies include NIRAMAI, Staqu, Qure.AI, Tech Mahindra, and DiyCam.
Professor Aboul Ella hassanien publications related to COVID-19 and Emerging Technologies such as AI, Machine Learning, Drones, Blockchain, IoT, Big Data
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Repurposed existing drugs and updated global health policy and clinical guidelines will be essential for limiting the social and economic devastation caused by this virus. So, we are leading a three-phase multinational Network Medicine clinical study (MNM COVID-19 study). The study will apply Network Medicine methodologies to repurpose existing drugs for SARS-CoV-2 infected patients and update global health policy and clinical guidelines.
Professor Aboul Ella hassanien publications related to COVID-19 and Emerging Technologies such as AI, Machine Learning, Drones, Blockchain, IoT, Big Data
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Repurposed existing drugs and updated global health policy and clinical guidelines will be essential for limiting the social and economic devastation caused by this virus. So, we are leading a three-phase multinational Network Medicine clinical study (MNM COVID-19 study). The study will apply Network Medicine methodologies to repurpose existing drugs for SARS-CoV-2 infected patients and update global health policy and clinical guidelines.
AI and covid19 | Mr. R. Rajkumar, Assistant Professor, Department of CSERajkumar R
SRM Institute of Science and Technology Directorate of Research presents Webinars on various domains. This is the slide presented by Mr. R. Rajkumar, Assistant Professor, Department of CSE,
Epidemic Alert System: A Web-based Grassroots ModelIJECEIAES
Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75 th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information.
Promise and peril: How artificial intelligence is transforming health careΔρ. Γιώργος K. Κασάπης
AI has enormous potential to improve the quality of health care, enable early diagnosis of diseases, and reduce costs. But if implemented incautiously, AI can exacerbate health disparities, endanger patient privacy, and perpetuate bias. STAT, with support from the Commonwealth Fund, explored these possibilities and pitfalls during the past year and a half, illuminating best practices while identifying concerns and regulatory gaps. This report includes many of the articles we published and summarizes our findings, as well as recommendations we heard from caregivers, health care executives, academic experts, patient advocates, and others.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16,
VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the Logistic Regression, which can predict death of an individual with a model accuracy of 94.40%.
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING DURING COVID 19q3technical
Artificial Intelligence (AI) and Machine Learning (ML) are the current buzzwords in the technology sector. They are not exactly same, but used synonymously by many. Both terms emerge around the subject: Analytics, Big Data or latest technological changes that are transforming our world.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
Paper: http://www.knoesis.org/library/resource.php?id=2153
Citation:
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini G. Forbis, Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children , IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA.
kHealth Bariatrics is an effort to bout against weight recidivism post bariatric surgery. The computer scientists working at Kno.e.sis, an Ohio Center of Excellence in BioHealth Innovation, are collaborating with a bariatric surgeon and a behavioural specialist to bolster weight loss surgery patients for appropriate postsurgical progress.
Digital technology and COVID-19
The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
Daniel Shu Wei Ting, Lawrence Carin, Victor Dzau and Tien Y. Wong, publicado en Nature Medicine.
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
AI, Machine Learning Playing Important Role In Fighting COVID-19 OpenTeQ group
This is example of research at the Radiological Society of North America. Highlighting the point out this phenomenon- that some of the patients are falling ill and dying as some others are experiencing very mild symptoms are none at all is the most mysterious element of the disease. Mortality is one of the correlations with some of the major factors like age, gender, and other major chronic conditions. Hence, more factors can be prognostic as the young individuals have succumbed to the virus.
No doubt, #healthcare is one of the most promising applications for #AI. This #technology can offer lots of benefits for this sector: it can help Health institutions to cut costs by lowering readmission rates, it can help insurance companies to optimize their risk management techniques, and it can also help doctors find new ways of healing.
Follow the link and learn more: https://indatalabs.com/blog/machine-learning-in-healthcare
AI and covid19 | Mr. R. Rajkumar, Assistant Professor, Department of CSERajkumar R
SRM Institute of Science and Technology Directorate of Research presents Webinars on various domains. This is the slide presented by Mr. R. Rajkumar, Assistant Professor, Department of CSE,
Epidemic Alert System: A Web-based Grassroots ModelIJECEIAES
Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75 th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information.
Promise and peril: How artificial intelligence is transforming health careΔρ. Γιώργος K. Κασάπης
AI has enormous potential to improve the quality of health care, enable early diagnosis of diseases, and reduce costs. But if implemented incautiously, AI can exacerbate health disparities, endanger patient privacy, and perpetuate bias. STAT, with support from the Commonwealth Fund, explored these possibilities and pitfalls during the past year and a half, illuminating best practices while identifying concerns and regulatory gaps. This report includes many of the articles we published and summarizes our findings, as well as recommendations we heard from caregivers, health care executives, academic experts, patient advocates, and others.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16,
VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the Logistic Regression, which can predict death of an individual with a model accuracy of 94.40%.
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING DURING COVID 19q3technical
Artificial Intelligence (AI) and Machine Learning (ML) are the current buzzwords in the technology sector. They are not exactly same, but used synonymously by many. Both terms emerge around the subject: Analytics, Big Data or latest technological changes that are transforming our world.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
Paper: http://www.knoesis.org/library/resource.php?id=2153
Citation:
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini G. Forbis, Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children , IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA.
kHealth Bariatrics is an effort to bout against weight recidivism post bariatric surgery. The computer scientists working at Kno.e.sis, an Ohio Center of Excellence in BioHealth Innovation, are collaborating with a bariatric surgeon and a behavioural specialist to bolster weight loss surgery patients for appropriate postsurgical progress.
Digital technology and COVID-19
The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
Daniel Shu Wei Ting, Lawrence Carin, Victor Dzau and Tien Y. Wong, publicado en Nature Medicine.
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
AI, Machine Learning Playing Important Role In Fighting COVID-19 OpenTeQ group
This is example of research at the Radiological Society of North America. Highlighting the point out this phenomenon- that some of the patients are falling ill and dying as some others are experiencing very mild symptoms are none at all is the most mysterious element of the disease. Mortality is one of the correlations with some of the major factors like age, gender, and other major chronic conditions. Hence, more factors can be prognostic as the young individuals have succumbed to the virus.
No doubt, #healthcare is one of the most promising applications for #AI. This #technology can offer lots of benefits for this sector: it can help Health institutions to cut costs by lowering readmission rates, it can help insurance companies to optimize their risk management techniques, and it can also help doctors find new ways of healing.
Follow the link and learn more: https://indatalabs.com/blog/machine-learning-in-healthcare
Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used...Bernard Marr
As coronavirus (COVID-19) swept from China to the rest of the world, emerging technologies such as artificial intelligence (AI), data science, and other technologies were thrust into action to help humans manage the crisis. Here are 10 ways technology is being used to manage and fight COVID-19.
Startups Step Up - how healthcare ai startups are taking action during covid-...Renee Yao
All around the world, people are facing unprecedented challenges and uncertainties as a result of COVID-19. At NVIDIA Inception program, a virtual incubation startup program, which hosts 5000+ AI startups, we see an army of healthcare AI startups that have mobilized to address this global health crisis. This webinar will share real world examples on how each offering plays a critical role during this pandemic.
Live event: https://www.meetup.com/Women-in-Big-Data-Meetup/events/270191555/?action=rsvp&response=3.
YouTube Link: https://www.youtube.com/watch?v=QWkKINi8u4o&feature=youtu.be
The 4 Top Artificial Intelligence Trends For 2021Bernard Marr
Artificial Intelligence (AI) has been a mega-trend in 2020. The current pandemic has only accelerated the relevance and adoption of AI and machine learning. Here we look at some of the top AI trends for 2021.
How machine learning is used to find the covid 19 vaccineValiant Technosoft
Let us understand the concept by taking an example of the deadliest pandemic of coronavirus. Machine Learning algorithms may help in detecting the severity of coronavirus in patients having the doubt of this deadly disease.
https://valianttechnosoft.com/blog/how-machine-learning-is-used-to-find-the-coronavirus-vaccine/
Artificial Intelligence in PharmacovigilanceClinosolIndia
The integration of Artificial Intelligence (AI) into pharmacovigilance has emerged as a transformative force, revolutionizing the monitoring and assessment of drug safety. This article provides a comprehensive overview of the application of AI in pharmacovigilance, elucidating its impact on the identification, evaluation, and management of adverse drug reactions (ADRs). AI-driven algorithms, machine learning, and natural language processing empower automated signal detection, enabling more efficient and proactive risk assessment. The review explores the utilization of AI in mining diverse data sources, including electronic health records, social media, and scientific literature, to enhance the sensitivity and specificity of ADR detection. Additionally, the article delves into the role of AI in streamlining case processing, automating data validation, and facilitating trend analysis, thereby optimizing the pharmacovigilance workflow. Challenges, such as data quality and interpretability of AI-generated insights, are critically examined, alongside ongoing efforts to address these concerns. The regulatory landscape and the incorporation of AI technologies into pharmacovigilance guidelines are discussed, highlighting the evolving framework for ensuring patient safety. As AI continues to evolve, its synergy with traditional pharmacovigilance practices opens new avenues for enhanced surveillance and proactive risk management in the dynamic field of drug safety.
How a U.S. COVID-19 Data Registry Fuels Global ResearchHealth Catalyst
In addition to driving COVID-19 understanding within the United States, a national disease registry is informing research beyond U.S. borders. Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone® COVID-19 data to develop a machine learning tool that helps predict the likelihood of COVID-19 mortality. With this national data set that leverages deep aggregated EHR data, the MOHT accessed the research-grade data it needed to build a machine-learning algorithm that predicts risk of death from COVID-19. The registry-informed prediction model was accurate enough to stand up to comparisons in the published literature and promises to help inform vaccine research and, ultimately, allocation of vaccines within populations.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Thai COVID-19 patient clustering for monitoring and prevention: data mining t...IAESIJAI
This research aims to optimize emerging infectious disease monitoring techniques in Thailand, which will be extremely valuable to the government, doctors, police, and others involved in understanding the seriousness of the spread of novel coronavirus to improve government policies, decisions, medical facilities, treatment. The data mining techniques included cluster analysis using K-means clustering. The infection data were obtained from the open data of the digital government development agency, Thailand. The dataset consisted of 1,893,941 cumulative cases from January 2020 to October 2021 of the outbreak. The results from clustering consisted of 8 groups. Clustering results determined the three largest, three medium-sized, and the two most minor numbers of infected people, respectively. These clusters represent their activities, namely touching an infected person and checking themselves. The components of emerging diseases in Thailand are closely related to waves, gender, age, nationality, career, behavioral risk, and region. The province of onset was mainly in Bangkok and its vicinity or central Thailand, as well as industrial areas. Adult workers aged 19 to 27 years and 43 to 54 years or over were seeds of new infection sources.
Here’s How All Of Us Can Use Technology To Help Tackle CoronavirusBernard Marr
Technology is used in different ways to help the world tackle coronavirus (COVID-19). In this article, we look at how everyone one of us can help in the fight using technology and crowdsourcing.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
2. AI – helping hand in the fight against Covid-19 Page 2
INTRODUCTION
The global medical sciences fraternity is working tirelessly to handle
COVID-19 ever since the outbreak was announced. Scientists have
been working tirelessly to sequence the genome of the novel
coronavirus, pharma giants have launched supportive therapies (e.g.,
Fabiflu and Remdesivir) in less than six months, and clinical trials of all
active vaccine candidates are taking an aggressive path.
Tied to these efforts is Artificial intelligence (AI) that has been
contributing significantly in expediting the efforts of handling this crisis. A
significant example is that of a Canadian AI-based company BlueDot,
which detected the outbreak of COVID-19 very early on.
3. AI – helping hand in the fight against Covid-19 Page 3
Figure 1 – AI domains
Prediction & Alerts
Diagnosis
Assistance & Prevention
Treatment & Cure
Research & Development
Other examples include use of AI in early diagnosis of coronavirus
infection by improving the speed and accuracy of imaging techniques,
deployment of AI-based robots to sterilize medical facilities, use of
drones to deliver medical supplies, identifying people’s compliance with
COVID-19 medical guidelines, and understanding and suggesting the
development process for vaccines and drugs. These are just a few
examples; the applications of artificial intelligence to fight against the
novel coronavirus is seemingly endless.
In this light, Bayslope has conducted a secondary research to identify
potential AI-driven solutions around the world to tackle COVID-19. Our
findings are presented in this report. The solutions are broadly classified
under five technology domains/ themes (shown in Figure 1 below).
Artificial
Intelligence
(AI)
combating
COVID-19
4. AI – helping hand in the fight against Covid-19 Page 4
1. PREDICTION &
ALERTS
2. DIAGNOSIS
This category captures concepts such as predicting a
disease outbreak, tracking its spread, and sending
alerts/ warnings to various healthcare systems,
agencies, and governments. The disease prediction may
be based on data collected from diverse sources such as
travel history, news articles, population density, disease
type, as well as real-time information.
For instance, Blue Dot, a Canadian start-up, developed
an alarm system that indicates a possible outbreak. This
system was released on December 31st, 2019, which
was earlier than the WHO announcement. The company
deployed its AI-based tools to analyze data and predict
the outbreak of COVID-19. The company analyzed
information related to airline ticketing, flight paths and
other data to accurately predict that COVID-19 would
jump from Wuhan to Bangkok, Seoul, Taipei, and Tokyo.
On similar lines, companies, such as Stratifyd and
Metabiota have been using AI tools to predict global
outbreaks of many infectious diseases.
This domain defines methods and/or applications that
help in the detection and identification of various
symptoms and causes of the disease. For instance,
various AI-based solutions have been used in
conjunction with CT scan devices, X-ray machines,
voice, and other medical devices to enhance the
accuracy and efficiency of the underlined diagnosis
methods.
Infervision, a Chinese start-up, offers a similar AI-based
solution to analyze CT scan images of the patients and
flag patients that are suffering from the disease.
5. AI – helping hand in the fight against Covid-19 Page 5
3. TREATMENT &
CURE
4. ASSISTANCE &
PREVENTION
Similarly, Chinese technology giant M3 Incorporation,
which is part of Alibaba Group Holdings, developed an
AI-based tool to analyze CT scan images to quickly
identify patients suffering from COVID-19.
This domain provides solutions that help in identifying
novel or repurposed drug targets and/or vaccines by
studying molecular details and other characteristics of
the target molecules. AI is leveraged to assist in the
study of the 3D structure of potential drugs and analyze
the performance of these drugs against their biological
targets.
Remarkable AI-based applications under this domain
include performing genome analysis, identifying
molecules for potential medications, antibody discovery,
developing RNA-based vaccines, predicting the structure
of virus associated with the disease such as coronavirus
protein structures, secondary structure of ribonucleic
acid (RNA) sequence of COVID-19, and more.
Google’s DeepMind uses deep learning to predict the
structure of proteins associated with SARS-CoV-2 and
the virus that causes COVID-19 to understand its
functioning. Further, Makers Lab’s, the R&D department
of Tech Mahindra, is deploying AI to conduct research
and develop potential drugs for the treatment of COVID-
19.
Assistance & Prevention is an equally important category
that captures all AI-based solutions that can help in the
prevention and assist during the tough times of an
outbreak. Various examples include the use of robots for
delivering medical supplies and essential needs, taking
6. AI – helping hand in the fight against Covid-19 Page 6
5. Research &
Development
care of patients, assisting doctors or nurses and
disinfecting rooms.
Similarly, drones have been used to deliver medical
supplies from one city to another city.
Another exemplary usage of AI in assisting and
preventing the intensity of an outbreak is the use of AI-
enabled thermal cameras to determine body temperature
of people at public places.
Diycam’s hospital management solution offers AI-based
solutions for healthcare institutes to check in real-time if
doctors and medical staff are wearing masks, caps,
gloves, etc. The underlined technology uses computer
vision, machine learning, and artificial intelligence to
collect, collate and analyze data from target sites, and
help medical authority keep a check on the status of
compliance.
Maccabi Healthcare Services, an Israel-based
organization, is yet another example that offers an AI-
based solution to identify people who are at maximum
risk of getting infected. The identification is done based
on the analysis of healthcare records of target people.
The category ‘Research and Analytics’ focuses on
collating and analyzing the necessary research records
that may act as a repository for researchers and
scientists to find a viable solution against COVID-19. For
instance, the Semantic Scholar team at the Allen
Institute for AI has partnered with leading research
groups to provide COVID-19 Open Research Dataset
(i.e. CORD-19), a free resource of more than 128,000
scholarly articles about the novel coronavirus. Similarly,
Innoplexus provide its Ontosight®
AI search platform that
7. AI – helping hand in the fight against Covid-19 Page 7
is freely available worldwide to researchers. The platform
provides latest global information around COVID-19.
8. AI – helping hand in the fight against Covid-19 Page 8
Artificial
Intelligence
is assisting
with population
screening,
drug discovery,
and resource
management
to fight against
COVID-19*.
*WHO declared in a digital health roundtable in June. 2020
9. AI – helping hand in the fight against Covid-19 Page 9
Potential AI-driven solutions around the world
Figure 2 – Geographical origin of AI solution providers for COVID-19
A massive pool of technology
companies, startups, research
organizations, and universities have
joined hands to work on AI-based
solutions in the fight against COVID-19.
Bayslope studied potential solutions
provided by various organizations from
this pool. Interestingly, the US alone has
contributed 28% of the available AI-
based solutions, followed by UK and
India (as shown in Figure 2). Majority of
these solutions are focused on
Diagnosis (36%), followed by
Predictions and Alerts (23%) and
Prevention and Assistance (20%), as
shown in Figure 3.
Figure 3 – Domains targeted by AI solution providers
for COVID-19
23%
36%
18%
20%
3%
Prediction and
Alerts
Diagnosis
Treatment & Cure
Prevention &
Assistance
Research &
Development
10. 1. Predictions & Alerts
Ada health
Ada Health has developed a
COVID-19 assessment app that
uses the amalgamation of medical
knowledge and probabilistic
reasoning to provide assessments
of a user's symptoms. This helps
provide measures to be taken in the
form of care navigation.
Courtesy – Ada health
Bluedot
BlueDot was among the first in the
world to run an AI-based warning
system to identify the emerging
risks of COVID-19 pandemic. It also
notified its clients through the
company's insight platform. BlueDot
insights send relevant and real-time
alerts to clients to help them easily
quantify their risk of exposure to
COVID-19.
Bespoke
Bespoke launched an AI chatbot
adviser - Bebot - to offer
coronavirus updates to travelers. It
also announced the launch of
BeAssist to help small businesses
create their own AI chatbox.
Courtesy – Bespoke
BlinkIn
BlinkIN offers Scotty, an AI and
Augmented Reality (AR) powered
live video calling system. This
system can be used to set up a
secure connection between
customer or field engineer and
remote experts. The company also
offers Huston, an AI-enabled self-
service system powered by a virtual
agent that allows users to leverage
the expertise of senior engineers
via smartphones.
11. AI – helping hand in the fight against Covid-19 Page 11
AirHealth
AirHealth’s virtual care platform,
Vital Health, provides early
detection of respiratory decline
(breathing disorder). It uses 35
proprietary and IP protected
biomarkers to assess different
dimensions of lung health and
process them in the AI engine to
categorize the severity of
symptoms. In addition, its remote
patient monitoring (RPM) vital sign
tool and a phone camera-based
warning system helps assess
patient decline.
Courtesy – AirHealth
Epic
The Epic AI model predicts the
severity of illness in Corona
patients. The model utilizes data
from more than 16,000 hospitalized
COVID-19 patients.
CLEW
CLEW’s AI-based predictive
analytics platform, CLEW-ICU,
provides early identification of
potential respiratory failure in
patients.
Courtesy – CLEW
Metabiota
Metabiota uses its AI-based
epidemic tracker platform to detect
the Coronavirus pandemic
outbreak. This platform uses a
digital surveillance system and a
global disease spread model to
predict epidemic spread.
SparkBeyond
SparkBeyond has leveraged its AI-
driven data analysis platform to
gather millions of data and has
created a dynamic, high accuracy
heat map that can predict where a
COVID-19 carrier is likely to pass. It
uses "blindfold analytics" that allows
it to create a model from sensitive
data, while restricting private data.
12. AI – helping hand in the fight against Covid-19 Page 12
Staqu
Staqu has an AI enabled video
analytics platform called JARVIS. It
is very effective in a pandemic
situation like COVID-19 enabling
face mask identification, social
distancing check, fever detection,
contact tracing and hygiene check.
Courtesy – Staqu
Stratifyd
Stratifyd AI scans social network
and sources such as the National
Institutes of Health, the World
Organization for Animal Health, and
the global microbial identifier
database to identify and predict the
real time updates of the COVID-19.
Traces.AI
"Traces.AI utilizes their AI model to
analyze videos using 2,000 different
attributes of a person. This analysis
helps trace people who have come
in contact with COVID-19 infected.
Courtesy – Traces.AI
Boston Children’s Hospital
Boston Children’s Hospital runs a
website called HealthMap that
utilizes artificial intelligence to scan
social media, news reports, internet
search queries, and other
information streams to gather
information related to the COVID-19
outbreak. Further, it maps the
spread of COVID-19 around the
globe.
Courtesy – Boston Children’s Hospital
13. AI – helping hand in the fight against Covid-19 Page 13
2. Diagnosis
BillionToOne
BillionToOne has developed a
qSanger-COVID-19 test that is
highly accurate and cost effective in
diagnosing a COVID-19 test.
Based on Sanger sequencer (a
method of DNA sequencing)
capacity from the Human Genome
project and the proprietary machine
learning algorithm, it unlocks
millions of daily testing capacity
worldwide.
Biobot Analytics
Biobot Analytics has launched a
COVID-19 sewage testing program,
that analyzes viruses, bacteria and
chemical metabolites that are
excreted via urine and stool into the
sewers. Based on the analysis, it
maps the data and helps
communities tackle public health
proactively.
The DAMO Academy
Alibaba's DAMO Academy has
developed an AI-enabled system
that could diagnose COVID-19
patients within 20 seconds, with
96% accuracy. The algorithm has
been trained with data and CT
scans of more than 5,000
coronavirus confirmed cases.
Butterfly Network
Butterfly Network has developed
'Butterfly iQ'- a handheld, AI-
powered ultrasound device that
sends images to a user’s mobile
phone for automated interpretation.
Courtesy – Butterfly Network
Cerebras Systems
Cerebras Systems supercomputer
CS-1 could be used to identify
treatments and drugs for COVID-
19. CS-1 uses its AI and Machine
learning to process massive
datasets in a fraction of time to
identify a suitable drug for the
pandemic.
14. AI – helping hand in the fight against Covid-19 Page 14
DarwinAI
DarwinAI has developed AI-based
tool named COVID-Net that is
capable of diagnosing COVID-19
patients by studying X-Rays and CT
scans of the chest. This is an open-
source tool.
The Co-founder and Chief Scientist
of DarwinAI said, “We made model
and data all available open source
and open access on GitHub… This
is the first time where an AI
explainability strategy is leveraged
to give deep insights into the visual
indicators that COVID-Net
leverages to make COVID-19
decisions, which will hopefully help
clinicians in better screening and
trust in the system.”
Infervision
Infervision has developed an AI
solution to identify COVID-19
patients by analyzing CT scans.
Doctors can quickly assess the
volume changes at different density
ranges from the histogram to
determine the progress of the
disease in follow-up studies.
Delft Imaging
Delft Imaging in association with
Thirona has developed the artificial
intelligence software CAD4COVID
that analyzes X-ray images to help
healthcare specialists manage
COVID-19 cases.
Dermalog
Dermalog has developed an AI-
enabled camera that measures
body temperature with outstanding
accuracy and speed even from a
distance of two meters. Since
nearly 90% of those infected are
diagnosed with fever, this system
can be used at restaurants, airports
etc. amid the COVID-19 outbreak.
Courtesy – Dermalog
Intellifusion
Intellifusion has launched an AI-
powered public health solution
which collects facial, temperature,
historical trajectory. It maps this
data of people to identify
symptomatic COVID-19 carriers.
15. AI – helping hand in the fight against Covid-19 Page 15
Huawei Cloud
Huawei Cloud has developed an
artificial intelligence (AI) tool that
utilizes medical imaging analysis to
diagnose corona cases. The AI-
assisted diagnosis service can
assess lung structure and
accurately distinguish between
early, advanced and severe stages
of the disease.
Nanox
Nanox has developed a mobile
digital X-ray system that uses AI
cloud-based software to diagnose
infections and help prevent
epidemic outbreaks. This X-ray
system incorporates a vast image
database and assistive artificial
intelligence systems, which
collectively assists for early
diagnosis of COVID-19.
Courtesy – Nanox
NIRAMAI
NIRAMAI has developed
FeverTest, an AI-based solution
that detects fever and COVID-19
respiratory symptoms. It enables
automated screening of people to
detect the likelihood of COVID-19
infected.
Seegene
Seegene has developed a COVID-
19 test kit called Allplex™ 2019-
nCoV Assay. Powered by its
proprietary AI-based data system,
this test kit quickly expands testing
capacity.
Subtle Medical
SubtlePET from Subtle Medical is
an AI-based software solution for
medical imaging that increases the
throughput of the analysis. It has
been playing an important role in
16. AI – helping hand in the fight against Covid-19 Page 16
the detection and diagnosis of
COVID-19.
Tempus
Tempus has leveraged its AI
technology to scrutinize its data
library and help physicians make
data-driven decisions when treating
COVID-19 patients. It has
equipped physicians with COVID-
19 PCR diagnostic testing
capabilities.
University of Copenhagen
University of Copenhagen’s AI
models calculate the risk of a
coronavirus patient to determine
whether the patent needs a
ventilator or intensive care.
Vocalis Health
Vocalis Health has developed an AI
model that collects voice samples
of coronavirus patients and healthy
individuals. The data collected
helps correlate the voice with
symptoms of coronavirus. This
enables early detection of via a
smartphone.
Aidoc
Aidoc uses an AI model to analyze
medical images to diagnose
COVID-19 patients.
ZOE
ZOE, in collaboration with
Massachusetts General Hospital,
King’s College London and the
University of Nottingham, has
developed an AI-based model that
determines the likelihood of a
COVID-19 infected person based
on symptoms. The AI model uses
data from the COVID-19 Symptom
Study app.
Diagnostic.AI
Diagnostic.AI utilizes its qPCR
technique to automate the PCR test
for COVID-19 using artificial
intelligence (AI).
Qure.AI
Qure.AI has developed an AI-based
solution called qXR that analyzes
chest X-rays of people to detect
abnormalities in lungs. It also
calculates the risk of COVID-19
infection.
Courtesy – Quer.AI
17. AI – helping hand in the fight against Covid-19 Page 17
3. Treatment & Cure
BenevolentAI
BenevolentAI utilizes a digital
storehouse of biomedical
information to identify data that can
be used by researchers to develop
vaccines and drugs. BenevolentAI
has provided studies on Baricitinib
being a potential drug to fight
COVID-19, which is at Stage 3 of
clinical testing.
Deargen
Deargen has used its pre-trained
deep learning-based drug-target
interaction model called Molecule
Transformer-Drug Target
Interaction (MT-DTI) to identify
commercial drugs that can be made
available as a potential drug for
COVID-19 treatment.
Courtesy – Deargen
Deepmind
Deepmind's deep learning solution
– AlphaFold - predicts protein
structures of the Corona virus
accurately, which eventually is
expected to aid the development of
tests and drugs.
Courtesy – DeepMind
Exscientia
Exscientia is using its AI-driven
drug discovery platform to examine
a collection of 15,000 potential
coronavirus disease treatments, in
collaboration with US research
institute Calibr and Diamond Light
Source.
Courtesy – Exscientia
18. AI – helping hand in the fight against Covid-19 Page 18
Healx
The Healx AI platform is helping to
identify drug combinations to target
the coronavirus and provide
immunity. Its AI platform, Healnet,
combines detailed biomedical
studies and provides combination
therapies for COVID-19 patients.
Courtesy – Healx
ImmunoPrecise
Using the discovery platforms and
AI capabilities with its partner
EVQLV, ImmunoPrecise is ready
with the PolyTope mAb Therapy
approach. This is expected to aid
the development of the universal
COVID-19 therapy.
Imperial College
Imperial College London, in
collaboration with Vodafone
Foundation, has launched a
Corona-AI research project using
the DreamLab App. This project is
using AI to go scrutinize data and
identify existing drugs that could
help develop vaccines and drugs
for COVID-19.
Insilico
Insilco Medicine has developed a
drug discovery engine that identifies
molecules to develop an effective
treatment against the coronavirus.
Vir Biotechnology
Vir Biotechnology, in collaboration
with GlaxoSmithKline plc (GSK), is
developing solutions for
coronaviruses. This solution will use
CRISPR screening and AI to
identify anti-coronavirus
compounds.
AbCellera
AbCellera is leveraging its
proprietary AI system to identify
antibodies created by the immune
system. This could be essential for
developing the COVID-19 drug.
Tech Mahindra
Tech Mahindra's R&D arm, Maker's
Lab, is conducting AI-based
research to find a potential drug to
fight COVID-19. The research team
has used molecular docking
techniques owing to the high
transmission rate of Covid-19.
19. AI – helping hand in the fight against Covid-19 Page 19
4. Prevention &
Assistance
Akara Robotics
As UV sterilization is preferable
over the chemical one, Akara
Robotics has come forward with a
UV Sterilizer robot VIOLET, which
has been clinically proven to kill
bacteria, viruses and other
pathogens. Also, it is effective on
the novel Coronavirus.
Courtesy – Akara Robotics
Antworks
Antworks, in collaboration with
Terradrone, has marked the launch
of the first urban air transportation
channel via drones. These drones
are being deployed in China for
rapid delivery of medical samples
and protection kits to hospitals.
Arone
Arone is deploying its drones to
deliver blood, vaccines and other
medical supplies to areas that are
highly impacted. Autonomous flight
navigation software uses computer
vision and AI for flight planning,
obstacle maneuvering and
detecting when a parcel is
delivered.
Blue Ocean Robotics
Blue Ocean Robotics makes
professional robots. Its subsidiary,
UVD Robots, has developed robots
that disinfects rooms and patients
with via UV sterilization methods,
making it very effective in fighting
against the novel coronavirus.
Courtesy – Blueocean Robotics
20. AI – helping hand in the fight against Covid-19 Page 20
Diagnostic Robotics
Diagnostic Robotics has developed
an AI-based platform that provides
continuous updates about the
progress and spread of the virus at
the community level. The company
has also developed a COVID-19
Remote Assessment and
Monitoring tool to help payers and
the government respond to each
other digitally, amid the outbreak.
Elliq
Elliq is an AI-powered solution
especially designed for senior
citizens. It helps them keep track of
their health during the pandemic.
Courtesy – Elliq
Hyro
Hyro has developed a
conversational AI tool, aiming at
clearing misunderstanding about
the global pandemic. This virtual
assistant processes questions
posted by users and delivers
intelligent responses to advance
their understanding, and advises on
the best course of action.
KenSci
KenSci has developed a leading AI-
based RealTime Command Center
and Hospital Capacity Planning tool
for COVID-19 Response. This tool
provides real-time view into hospital
operations, such as bed
management and capacity
planning.
KroniKare
KroniKare is developing an AI-
powered temperature screening
solution named iThermo that
screens and identifies people with
fever symptoms.
Courtesy – KroniKare
21. AI – helping hand in the fight against Covid-19 Page 21
Terra Drone
Terra Drone has collaborated with
Antworks to transport medical
samples to hospitals in China via
drones. Further, its drone has been
helping Nur-Sultan’s (Kazakhastan)
police department in patrolling the
city.
ForwardX Robotics
ForwardX Robotics has launched
Robotics-as-a-service as its AI-
enabled Robots in this pandemic
situation. It aims to boost
productivity by automating supply
chains.
Courtesy – ForwardX Robotics
DiyCam
Diycam utilizes AI enabled cameras
to check monitor medical staff and
doctors’ usage of face masks,
gloves and aprons inside ICUs.
5. Research &
Development
Allen institute
A Semantic Scholar team at Allen
Institute has prepared the COVID-
19 Open Research Dataset
(CORD-19). The team has
partnered with leading research
groups to provide CORD-19, a free
resource of more than 128,000
scholarly articles about the novel
coronavirus for use by the global
research community.
Innoplexus
Innoplexus provide its free
Ontosight AI search platform for
relevant to COVID-19 researchers
and public across the world. The
platform provides latest global
information around COVID-19.
Courtesy – Innoplexus
22. AI – helping hand in the fight against Covid-19 Page 22
6. PATH AHEAD
In these uncertain times, when we
are still researching to find the
appropriate drug or vaccine,
artificial intelligence is playing a
dynamic role in researching a path
towards recovery.
Researchers embraced the
potential of using AI to come up
with innovations that can support
the global population to fight
against the pandemic situation
caused by COVID-19.
Artificial Intelligence has helped
researchers across the globe
expedite and streamline the overall
process involved in drug designing
and development of vaccines to
combat/ handle Covid-19.
As on date (please mention the
date of study), we have 17 vaccine
candidates, as published by the
WHO in July 2020.
In addition, supportive therapies
with Fabiflu and Remdesivir are
ensuring a high recovery rate and
an overall decrease in mortality
rate.
23. AI – helping hand in the fight against Covid-19 Page 23
Have any Query?
Get in touch with us.
For further information or if you a detailed report about the patents of these
companies around the discussed solutions, please write to us at
contact@bayslope.com.
www.bayslope.com
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