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SINGAPORE - At least two universities here are turning to technology to send students into a lockdown when they take online examinations, to prevent them from cheating.

At the Singapore Management University (SMU) and Singapore Institute of Technology (SIT), students web browsers are locked, such that they will not be able to access other websites or capture screenshots until they have completed the exam.

Before the exam, students will also have to take a short video of their location, such as the room and study table.

During the exam, they use a webcam to record themselves. An artificial intelligence algorithm will track their eye movement to determine where and what they are looking at, to deter cheating.

After the exam, only the course instructor can review video recordings and the results of the proctoring or invigilating session and video segments. Potential violations, if any, are then flagged.

Universities in Singapore have moved to online lessons and exams due to the coronavirus outbreak and circuit breaker measures.

Online exams at SMU have been running from April 13, and will end on Friday (April 24). SITs exam period is from April 27 to May 8.

In response to queries from The Straits Times, an SMU spokesman said on Thursday (April 23) that the university has been using this tool for online exams since two years ago, but on a much smaller scale.

At the time, it was "mainly to support students who were unable to take their tests on campus, such as due to illnesses or participation in overseas competitions".

"We have scaled it up this round to facilitate online undergraduate and postgraduate closed-book exams. More than 100 instructors have used the tool in the past week," said the SMU spokesman.

The Straits Times understands that so far students have had no issues with their webcams, and there have been no requests for equipment support.

Associate Professor Lieven Demeester, who teaches business modules at SMU, said he used the tool for the first time at the start of the month to conduct a 2 hour exam for 40 students.

"I gave them instructions in advance on what I wanted to see in their videos - my own view of what I think is a secure work environment. I asked them to show the underside of their tables and chairs, and they also had to film their pockets so I could make sure they didn't have anything in them," Prof Demeester told ST.

After the exam, he scrolled through the videos for all 40 students, which took him about 1 hours. There were a set of thumbnails, or pictures of students taken at various intervals, he added.

"I don't watch every second of every video. The programidentifies major changes and highlights parts where someone is moving or someone else comes into the frame, but these are rare occurrences."

He noted that the tool was an effective deterrent for cheating that also provides a mechanism to follow up on potential breaches.

"When the camera is on you, if you're looking away from the screen, its very visible. In a classroom, there's usually only one invigilator, and you can never be looking at every student all the time. But here, we can, even though it's after the fact," Prof Demeester said.

He noted that students looking away from their screens may not necessarily be trying to cheat.

"You'll have to look a bit closer at the video to see if it re-occurs or if there's a pattern (of them looking away). But I've been lucky, none of my students have shown such behaviour."

The SMU spokesman added that mock tests are created before the online exam for students to prepare themselves and their laptops in advance.

Follow-up IT support is also available to students who have difficulty with their set-up.

The spokesman noted that the tool "is by no means a guarantee against acts of cheating, just like in normal exams and graded assignments".

"Typically, instructors are mindful of this and will take special care to set questions that can minimise cheating and to spot suspicious similarities in answers for further investigation."

A National University of Singapore spokesman told ST the university has put in place measures, such as online proctoring, to preserve the integrity of online assessments.

"Students have been reminded of the serious consequences, including suspension or expulsion, if they are found responsible for any academic misconduct," the spokesman said.

Besides measures to prevent cheating during online exams, universities here also use software to check assignments that are submitted online.

ST understands that the Singapore University of Social Sciences, for instance, uses a plagiarism checker software to check online assignments.

The Singapore University of Technology and Design does not have any online exams for this term.

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Coronavirus: Universities use artificial intelligence to deter cheating in online exams - straits times

NEW YORK, April 22, 2020 /PRNewswire/ --

KEY FINDINGSThe global artificial intelligence in healthcare market is set to register growth, projecting a CAGR of 38.05% during the forecast period, 2020-2028. The prominent drivers of market growth are estimated to be the rising big data in the healthcare industry, the growing use of AI in genetics, the emergence of personalized medicine in tests for clinical decision making, along with the creation of a real-time monitoring system due to AI.

Read the full report: https://www.reportlinker.com/p05242360/?utm_source=PRN

MARKET INSIGHTSThe utilization of AI in healthcare entails the use of software and algorithms for estimating the human perception for analyzing complex medical data, along with the relationship between treatments or prevention techniques and patient outcomes.The growing demand for real-time monitoring system is one of the key aspects propelling the growth of the global artificial intelligence in healthcare market.

The real-time monitoring devices like health monitoring devices or indicators track real-time health data of patients, which is increasing the demand for AI in healthcare.The devices also drive the relevancy of data interpretation and aid in reducing the time the patients spend in piecing data output.

In healthcare, the devices help in detecting and preventing undesirable patient outputs. The growing number of mobile devices integrated with artificial intelligence assists in the prediction of future outcomes with regard to health, which further benefits market growth.Medical practitioners are reluctant to adopt AI-based technologies, and this is restraining the growth of the market.The reluctance is because of the lack of data that identifies healthcare decisions.

Also, from a diagnostics point of view, AI systems fare less in terms of efficiency in comparison to conventional methods.The companies in the market are competing against each other by providing the same characteristics and similar prices.

The competitive rivalry is projected to be high during the forecast period.

REGIONAL INSIGHTSThe geographical segmentation of the global artificial intelligence in healthcare market includes the analysis of Europe, North America, Asia Pacific, and the rest of the world.Inkwood Research estimates the Asia Pacific region to be the fastest-growing region by the end of the forecast period.

The invention of new technologies, the presence of countries like China, Japan, Australia, and India, and the thriving artificial intelligence market, are the factors propelling the growth of the market.

COMPETITIVE INSIGHTSSome of the prominent companies operating in the market are Enlitic Inc, Next IT Corporation, Recursion, Welltok, GE Healthcare, Microsoft Corporation, etc.

Our report offerings include: Explore key findings of the overall market Strategic breakdown of market dynamics (Drivers, Restraints, Opportunities, Challenges) Market forecasts for a minimum of 9 years, along with 3 years of historical data for all segments, sub-segments, and regions Market Segmentation cater to a thorough assessment of key segments with their market estimations Geographical Analysis: Assessments of the mentioned regions and country-level segments with their market share Key analytics: Porter's Five Forces Analysis, Vendor Landscape, Opportunity Matrix, Key Buying Criteria, etc. Competitive landscape is the theoretical explanation of the key companies based on factors, market share, etc. Company profiling: A detailed company overview, product/services offered, SCOT analysis, and recent strategic developments

Companies mentioned1. DEEP GENOMICS INC2. ENLITIC INC3. GE HEALTHCARE4. GENERAL VISION INC5. GOOGLE6. IBM CORPORATION7. ICARBONX8. INTEL CORPORATION9. MICROSOFT CORPORATION10. NEXT IT CORPORATION11. NVIDIA CORPORATION12. ONCORA MEDICAL13. RECURSION PHARMACEUTICALS INC14. STRYKER CORPORATION15. WELLTOK INC

Read the full report: https://www.reportlinker.com/p05242360/?utm_source=PRN

About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

__________________________ Contact Clare: [emailprotected] US: (339)-368-6001 Intl: +1 339-368-6001

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The global artificial intelligence in healthcare market is set to register growth, projecting a CAGR of 38.05% during the forecast period, 2020-2028 -...

By Maj William Giannetti, USAFR*

The DODs new artificial intelligence (AI) strategy is a treasure trove of ideas.1 Unveiled during a February 2019 press conference, it is (to put it mildly) an ambitious document, and its implications are far-reaching. In a departure from hard-coded garbage-in, garbage-out programs that burp out specific output, algorithm writers will craft code that learns on its own. Neural networks modeled after biological systems might one day roam the gray areas of human thought. With time and considerable training, AI will discern tanks from trucks or MiGs from run-of-the-mill airplanes.

Autonomous vehicles will transport troops to the frontlines, and someday pilotless aircraft might transport cargo and refuel fighters. Developmental Air Force AI already enables semiautonomous loyal wingmen, guided by pilots, to carry out preprogrammed missions from the relative safety of their cockpits.2 Later, faulty parts imbued with AI would speak out when their replacement comes due, making maintenance schedules more efficient and less costly. Military doctors might recommend an early biopsy after an AI-assisted ultrasound detects disease, thus improving prognoses so that all Americans might live longer, fuller lives.

Air Force generals presently envision a world where AI rapidly transforms data into knowledge that accurately informs a human-led decision-making process.3 We need our analysts to harmonize the data-to-decision quality at speed, said Air Force Director of Intelligence Lt Gen VeraLinn Dash Jamieson during an interview at Goodfellow AFB,Texas in 2017.We must build the next generation ISR enterprise capable of possessing decision advantage across the entire spec- trum of conflict.4

But to get there, developers require a preaccredited, flexible cloud to cultivate the AI strategys ideas, lest they die an untimely death on the policy vine. Another must is a secure DOD cloud that stores the considerable quantity of data that would fuel the nations AI and machine learning algorithms. Skeptics say the piles of servers and processors it would take cost billions. But a partnership between Lt Gen John N. T. ( Jack) Shanahans new Joint Artificial Intelligence Center ( JAIC) and a little-known Air Force cloud service called TITAN (Technology for Innovation and Testing on Accredited Networks) could bring value while making everyones AI dreams come true for a fraction of the cost. First, lets put cloud computing in context by looking at its costs and the role it plays in managing the Pentagons IT.

According to the Government Accountability Office, the federal government invests more than $90 billion annually in the development, implementation, and maintenance of IT infrastructure.5 To offset this cost, the Office of Management and Budget debuted its Cloud First Strategy, which mandated agencies pool IT resources in secure, efficient, and cost-effective ways.6 Cloud computing eliminates storing data on bare metal, stand-alone hard drives and shifts the burden to groupings of software and high-capacity storage servers. A clouds elasticity allows administrators to add (or subtract) storage and computing power while public and private user groups lend it scalability.

The DOD went all in with the cloud, investing $2.7 billion between 201518. Its subordinate organizations operate an estimated 500 clouds, and as of 2019, the Pentagon racked up 88 cloud investments out of 2,735 for IT overall.7 The sheer number of clouds managed by multiple vendors poses a growing administrative headache. The Joint Enterprise Defense Infrastructure ( JEDI) initiative, with its estimated $10 billion price tag over 10 years, seemed to be the cure. Businesses from across the tech community flocked to Washington with their proposals. Microsoft, a decades-long mainstay of government IT, recommended Azure for JEDI. Amazon Web Services (AWS), a relative upstart, offered its seemingly infinite storage capacity.

AWS was a favorite to win because it gained the governments confidence in storing sensitive information and programs.8 Engineers from the private and government sectors use AWS SageMaker to create machine-learning algorithms with drag-and-drop ease. Clouds would consolidate under JEDIs umbrella and lessen confusion as the department transferred its oldest legacies into it.9 One set of tools and standards for AI (or other software development for that matter) affords engineers a shared environment to discover information and create algorithms.

Traditional computer firms, like IBM and other Silicon Valley players like Oracle, have lodged complaints. They claimed awarding JEDI to a single company unfairly stifles competition and makes the militarys cloud especially vulnerable to Russian and Chinese cyberattacks.10 The arguments soon intensified and took a more personal turn. President Donald Trumps feud with Amazon CEO Jeff Bezos spilled into public view, and in a surprise ruling Microsoft was granted the huge contract. In its appeal to a U.S. federal court, Amazon says political influence tipped the Pentagons decision, and that procurements should be administered objectively.11

Meanwhile, as corporate and government lawyers do battle, an average-looking industrial building sits tucked into the scrub pines and dogwood trees of Fort Belvoir, Virginia. Inside one of its air-cooled rooms, chilled to 65 degrees Fahrenheit, are dozens of repurposed computer servers quietly whirring away. To the average onlooker, the sight might seem unimpressive, but this is TITAN, a government-owned, contractor-operated cloud worth $18 milliona veritable shoestring compared to JEDI. The US Air Forces ISR Innovations Directorate founded TITAN in 2016. It is funded entirely by Headquarters Air Forces ISR chief information officer and maintained by a handful of defense workers.

TITAN is unique because it is a hybrid cloud, a place where engineers rapidly prototype and deploy their software or custom applications. At 7.6 petabytes, it is modestly sized and ideal for the JAICs specialized work. To the layman, a petabyte might not seem like much, but its a very sizable chunk of data. Back in 2013, the Air Forces Distributed Common Ground System was processing 1.3 petabytes per month, which equates to about 1,000 hours of full-motion video per day.12 By comparison, in 2014, Facebooks massive 1.2 billion user base was generating four new petabytes of content per day.13

A hybrid cloud combines the best of private and public clouds. Public clouds combination of hardware, software, and storage services are managed by a third party while private clouds are sequestered from the public and protected by a firewall. Combining public services with private clouds and the data center as a hybrid is the new definition of corporate computing, says Judith Hurwitz of Hurwitz and Associates, an IT consulting firm. Not all companies that use some public and some private cloud services have a hybrid cloud. Rather, a hybrid cloud is an environment where the private and public services are used together to create value.14

Top cloud competitors AWS and Microsoft Azure offer a combination of physical and virtual suites, too. They bill their customers on a monthly pay-as- you-go basis. While AWS typically charges customers by the hour, Microsoft Azure and its Machine Learning Service charge by the minute. The attraction to AWS stems from its unalloyed computing power. Depending upon the customer, it can increase scale to thousands of machines and weave neural nets that far exceed TITANs limit. Azure, on the other hand, is less hardware intensive. Customers can have as many virtual machines as they like. Simple to use cookie-cutter software loads make start-up easy. And both firms enable the fast-paced development-to-operations (DevOps) culture that pervades software development and AI today.

But AWS and Microsoft Azure create vendor lock-in, which eventually commits (or locks) customers into using their specific proprietary tools indefinitely. Not TITAN. Its value to the JAIC comes from its agnostic nature, where users are in control. They can choose either the Microsoft or Linux operating systems for DevOps, at no monthly or daily expense, and with zero strings attached.

And, unlike the typical government 1990s-style data center where IT support occurs in-house (or, on-premises), TITAN is managed off-premises. Its servers are separate from the Pentagon but kept secure and effectively reachable by all its customers. TITANs almost two dozen customer agencies can access 430 data feeds via virtual machines worldwide and develop custom software without purchasing additional equipment.

Portability is a plus, too, because administrators can log in almost anywhere to diagnose problems, upload software patches, make updates themselves, or automate the tasks. As an added benefit, like AWS and Microsoft Azure, TITAN has authorities to operate on the DODs unclassified and classified systems, an essential requirement for clouds, according to former Deputy Secretary Patrick Shanahans Cloud Executive Steering Group.15 With a flexible, preaccredited cloud that provides developers value and relative cost savings to the taxpayer, the JAICs choice is clear. A TITAN partnership will help the Pentagon discover the AI advances of tomorrow to improve Americas security and quality of life today.

Disclaimer: The views and opinions expressed or implied in the Journal are those of the authors and should not be construed as carrying the official sanction of the Department of Defense, Air Force, Air Education and Training Command, Air University, or other agencies or departments of the US government.

*About the author: Major Giannetti (MS, St. Josephs University) is a reserve officer assigned to the Headquarters Air Force staff at the Pentagon.

Thisarticlewas originally published in theAir and Space Power JournalVolume 34, Issue 1, Spring 2020 (PDF).

Notes:

1. Summary of the 2018 Department of Defense Artificial Intelligence Strategy: Harnessing AI to Advance Our Security and Prosperity, 12 February 2019, https://media.defense.gov/.

2. Andrew Liptak, Skyborg Could Let F-35 and F-15 Fighter Jets Control Their Own Companion Drones, Verge, 22 May 2019, https://www.theverge.com/.

3. Air Superiority 2030 Flight Plan: Enterprise Capability Collaboration Team, May 2016, https:// http://www.af.mil/.

4. Oriana Pawlyk, China Leaving US Behind on Artificial Intelligence: Air Force General, Military.com, 30 July 2018, https://www.military.com/.

5. US Government Accountability Office (GAO), Cloud Computing: Agencies Have In- creased Usage and Realized Benefits, but Cost and Savings Need to Be Better Tracked, Report to Congressional Requesters, April 2019, https://www.gao.gov/.

6. Federal Cloud Computing Strategy, From Cloud First to Cloud Smart, accessed 24 Oc- tober 2019, https://cloud.cio.gov/.

7. DoD Cloud Update, Deputy Secretary of Defense, 22 June 2018, https://federalnews network.com/. See also GAO, Cloud Computing: Agencies Have Increased Usage and Realized Benefits, but Cost and Savings Need to Be Better Tracked, Report to Congressional Requesters, April 2019, https://www.gao.gov/.

8. Frank Konkel, Tech Firms Ask Congress to Intervene on Pentagon JEDI Contract, Next- Gov, 30 April 2018, https://www.nextgov.com/.

9. Jack Moore, Here Are 10 of the Oldest IT Systems in the Federal Government, NextGov, 25 May 2016, https://www.nextgov.com/.

10. GAO, Decision in the Matter of International Business Machines Corporation, 11 De- cember 2018, https://www.gao.gov/. See also GAO, Decision in the Matter of Oracle America, Inc., 14 November 2018, https://www.gao.gov/.

11. Aaron Gregg and Jay Greene, Fierce Backlash Against Amazon Paved the Way For Microsofts Stunning Pentagon Cloud Win, Washington Post, 30 October 2019, https://www .washingtonpost.com/. See also Jared Serbu, Amazon to Protest DoDs JEDI Cloud Contract, Federal News Network, 14 November 2019, https://federalnewsnetwork.com/.

12. Marc V. Schanz, ISR After Afghanistan, Air Force Magazine, January 2013, http://www .airforcemag.com/.

13. Janet Wiener and Nathan Bronson, Facebooks Top Open Data Problems, Facebook Re- search, 22 October 2014, https://research.fb.com/.

14. Judith Hurwitz et al., What is Hybrid Cloud Computing? Dummies, https://www .dummies.com/.

15.DOD,AcceleratingEnterpriseCloudAdoption,15February2018,https://dod.defense.gov/.

Please Donate Today Did you enjoy this article? Then please consider donating today to ensure that Eurasia Review can continue to be able to provide similar content.

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Quiet Giant: The TITAN Cloud And The Future Of DOD Artificial Intelligence Analysis - Eurasia Review

April 22, 2020 - With artificial intelligence coming up more and more frequently in every area of everyday lifefrom transportation, grocery shopping to Alexa and Siri it might be natural assume that the technology would be helpful in addressing the coronavirus.

That at least so far hasnt turned out to be true, said Alex Engler, a Brookings Institution fellow of government studies.

There have been small pockets of COVID-19 relief in which AI has been helpful at the margins, such as in assisting the construction of some disease spread models.

And there are hypothetical projects where it could be helpful, like to improve the accuracy of COVID-19 testing in conjunction with CT scans and in modeling the protein structure of the virus.

But mostly, the media has been rife with misleading or inaccurate claims Engler called them snake oil about the ability of AI to address the crisis.

Engler spoke about the claim that AI and thermal sensors could sense the presence of COVID-19 in individuals as an example of misinformation in the media.

He sighed when fellow panelist Michelle Richardson, director of the Data and Privacy Project at the Center for Democracy and Technology, referenced an article claiming that AI is being used to tell whether or not citizens are wearing a facemasks.

All currently impossible, and probably unethical, said Engler. For issues such as contact tracing, its still going to take an army of contact tracers, said Brookings Institution fellow Nicol Turner Lee.

The panel then turned to contact tracing and its hot new cousin: Proximity tracing.

Panelists Engler and Richardson debated the feasibility of Apple and Googles proposed proximity tracing applications, in which the spread of disease isnt tracked by individual locations but by the Bluetooth activation on devices that come within a certain distance of each other.

Individuals tested positive for the virus can then send an alert to every device that came into contact with that of the infected persons and tell them that they may have been exposed to the coronavirus.

Engler laid out the barriers: for proximity tracking to work: Americans would have to update their operating system, download an app, and consent to its privacy policy.

Then he laid out the numbers: 81 percent of Americans own smartphones, according to Pew.

Of those, how many would download the app? And then of those, how many people will report that they were sick? And then that number would need to be squared because it takes two to transmit, Engler said.

Richardson identified more practical problems, such as the utility of the model on asymptomatics.

But Engler pushed back against that notion. Proximity tracing would be helpful for asymptomatic because it would give them a good idea of whether of not they are silent carriers and would inform them on how careful they should be in their interactions.

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Artificial Intelligence Not Very Helpful in Addressing the Coronavirus, Say Experts on Brookings Panel - BroadbandBreakfast.com

Developing an effective vaccine for the current pandemic as well as treatment options for COVID-19 patients is easier said than done. The drug discovery and development processes are by no means a walk through the park, even after a potential lead is identified, countless hurdles still need to be overcome before any drug makes it to the public.

Traditional new drug development is an expensive process and is comprised of a discovery phase that includes target-based drug screening and optimization, among other processes to identify candidates to advance towards further development. Subsequent drug development involves drug combination design with these candidates, and clinical trials. Unfortunately, success rates are very low, said Dean Ho of the National University of Singapore. Ho and his collaborator, Professor Xianting Ding of Shanghai Jiao Tong University, have turned to AI to solve this problem.

For rapidly spreading pathogens with unpredictable clinical courses, [such as the current SARS-CoV-2 outbreak], this process takes too long, even with the assistance of emerging technologies, added Ho.

Even re-purposing known drugs for combination therapies can be quite challenging as choosing the right combination as well as dosage precludes the optimization of treatment outcomes, said Ho. This also limits how many drugs can be simultaneously explored, as conventional drug screening protocols cannot cope with the large data pools that get generated as a result. Given this challenge, traditional new drug development and traditional repurposing are inherently sub-optimal, he added.

In a recent paper published in Advanced Therapeutics, the team developed an AI-based platform called Project IDentif. AI that was shown to quickly screen and identify viable combination therapies for the past, present, and future infections.

Using traditional re-purposing to address SARS or MERS would be very challenging due to the aforementioned issues, said Ding. However, with a platform like IDentif.AI, combination therapy optimisation could be accomplished within days. IDentif.AI is a disease-agnostic platform. As such, it does not have to be reprogrammed, and can be immediately deployed against any novel or established pathogen.

According to the team, the core importance of IDentif.AI is that it simultaneously reconciles the optimal drugs and doses against virtually any disease model from the aforementioned extraordinarily large drug/dose parameter spaces. When good drugs are given at the wrong dose, there may be no treatment efficacy at all, said Ho. At the same time, drug dosing may also have a role in determining which drugs belong in a combination in the first place. Therefore, simultaneously pinpointing the right drugs and doses is absolutely essential.

To run a search, a small set of pre-designed combinations of drugs is given to provide a sample of the drug-dose parameter space. Imagine filling up an entire room with tiny marbles, with each marble representing a possible drug-dose combination. Our job is to find ranked list of best to worst marbles from a room filled with billions of them. This pre-designed set of combinations doesnt pinpoint every single one of them, but at least samples enough of the space to guide us to where the best one is and tells us the drugs/dosages of that optimal combination, explained Ding.

After this first set of experiments is done and the full drug-dose space is essentially mapped out for us, IDentif.AI operates off the concept that drugs and doses (inputs) are related to treatment outcomes (e.g., antiviral activity, drug toxicity) using a smooth quadratic surface (resembling a smooth mountain with one peak), added Ho. This surface is calibrated and mapped out by these set of unique initial experiments such as preventing a virus from infecting a healthy cell or shrinking a tumour (maximizing efficacy), or preventing healthy cell death (minimizing toxicity), etc.).

The map is therefore unique to every study, using different drugs and disease models, and can represent a population of cells, animals, people, or even a single patient, says Ding. To develop a population-optimized regimen, we can take biological samples pooled from a population of patients. This pooled sample can be run against a standardized cell infection model and within days, a combination will be derived. The surface map will be based on the viral/infected cell population represented by a large population of patients.

For a personalized case, if there is a patient with a high viral load, we can run the test using only their own sample, and within days, we can develop a regimen just for that patient, and the surface map will be represented by only their own sample.

And not every single drug combination needs to be screened, as once the team runs a threshold number of experiments, the map can be created and used to guide the team through the rankings of best to worst combinations based on optimal inhibition of infection and minimal toxicity.

What is really neat about IDentif.AI its ability to interrogate such as huge drug-dose space, which has already directly led to successful clinical outcomes and other indications, said Ho. As proof of concept, the team was able to identify an effective combination therapy that successfully inhibited A549 lung cell infection by the vesicular stomatitis virus (VSV) within three days of project. This compared to the months or even years that conventional drug discovery searches require, which are still only capable of exploring a tiny chemical space with poor clinical outcomes, demonstrates this technologys critical importance.

The reality is that the world will be confronted with challenges such as COVID-19 again, said Ho. We simply dont have the time or resources to wait for vaccines or antibody therapy every time. Lessons learned from COVID-19 have shown us that we cannot continue to relinquish valuable time in identifying optimal repurposed combinations. This will not solve the problem and will lead to drug shortages when some could have in fact been used correctly if a systematic optimization process was conducted.

IDentif.AI is also being adapted for additional pathogens such as Dengue fever and even the possibility of a SARS-CoV-2 mutation. If it mutates to a stage where a novel combination will be needed, IDentif.AI will be prepared to rapidly respond, said Ho.

Our aim is to give Project IDentif.AI to the world so that the next epidemic can potentially be contained or prevented using rapidly optimized drug repurposing. Implementing IDentif.AI is remarkable, rapid, and economical. As such, our work has involved healthcare economics, global health security, and surveillance experts to help us develop strategies to scale this towards widespread use on a cost-neutral basis.

More:
Recruiting artificial intelligence in battle against COVID-19 and future pandemics - Guardian