AI Powered Custom COVID 19 Healthcare Bot

The CDC COVID19 Healthcare bot 

The U.S. Centers for Disease Control and Prevention (CDC) is using Microsoft's Healthcare chat bot service to power its own COVID-19 assessment bot. The CDC's COVID-19 assessment bot is here. 

The CDC's COVID-19 bot is meant to quickly assess symptoms and risk factors and suggest a next course of action (like see a doctor or just stay home). Microsoft's Healthcare Bot runs on Azure and was first made publicly available in February 2019.

The bot service originally  began as a research project in 2017. That bot service allows users to create chat bots and AI-powered health assistants using Microsoft's service. 

The Healthcare Bot service can integrate with Electronic Health Records. In addition to the CDC, customers using this service to build their own bots include Quest Diagnostics and Kaiser Permanente. Providence St. Joseph Health also is using a COVID-19 screening service built using Microsoft's Healthcare bot technology.

Microsoft's AI powered Healthcare bot service is meant to guide customers via a natural conversation experience. It is customizable so that it can fit in with an organization's own scenarios and protocols.

In addition to the underlying bot service, several customizable COVID-19 response templates have also been made available. These include a COVID-19 risk assessment based on CDC guidelines; COVID-19 clinical triage based on CDC protocols; COVID-19 answers to frequently asked questions; and COVID-19 worldwide metrics.

An Overview of the Microsoft Healthcare Bot

Conversational AI for Healthcare: A cloud service that empowers healthcare organizations to build and deploy, AI-powered virtual health assistants and chatbots that can be used to enhance their processes, self-service, and cost reduction efforts.

Built-in healthcare intelligence: The Healthcare Bot comes with built-in healthcare AI services, including a symptom checker and medical content from known industry resources, and language understanding models that are tuned to understand medical and clinical terminology.

Customizable: You will receive your own white-labeled bot instance that can be embedded within your app or website. You can customize the built-in functionality and extend to introduce your own business flows through simple and intuitive visual editing tools.

Compliance: The service aligns to industry and globally recognized security and compliance standards such as ISO 27001, 27018, and CSA Gold and GDPR and provides tools that help our partners create HIPAA compliant solutions. 

Out-of-the-box AI and world knowledge capabilities: While each health bot instance is highly customizable and extensible, the Health Bot Service is built with a wide range of out-of-the-box features. 

• The Health Bot Service leverages information from respected healthcare industry data sources to generate accurate and relevant responses. 
• The Health Bot Service enables meaningful conversations for patients with an interactive symptom checker and uses medical content databases to answer health questions. 
• Conversational intelligence supports layperson natural language conversations to flow and adapt dynamically as each health bot instance learns from previous interactions. The service intelligence is powered by Microsoft Cognitive Services and credible world knowledge.

Configurable and extensible:

The Health Bot Service provides endless flexibility of use to Microsoft partners:

• Unique scenarios can be authored by partners for their health bot instances to extend the baseline scenarios and support their own flows.
• The health bot instance's behavior can be configured to match the partner's use cases, processes, and scenarios.
• The health bot instance can easily be connected to partners' information systems---for example, systems that manage EMR, health information, and customer information.
• The health bot instance can be easily integrated into other systems such as web sites, chat channels, and digital personal assistants.

Security and Privacy: The information handled by each instance of the Health Bot Service is privacy protected to HIPAA standards and secured to the highest standards for privacy and security by Microsoft. 

Built on top of Microsoft Azure technology, the Azure architecture powers the Health Bot Service's ability to scale with resilience, while maintaining the highest standards of privacy and security.

Easy to manage: Each health bot instance is easily managed and monitored by Microsoft partners via the Health Bot Service's management portal and management API. The management portal provides the ability to define the health bot instance's behavior in fine detail and to monitor usage with built-in reports. Management API allows the partner to embed the health bot instance and to securely exchange data and information.

Common Use-case scenarios:

The Health Bot Service contains built-in scenarios. Additional scenarios may be authored through the Scenario Editor.

The built-in scenarios include the following:

• Triage/symptom checker, powered by built-in medical protocols: The end user describes a symptom to the health bot instance and the bot helps the user to understand it and suggests how to react; for example, "I have a headache."
• General information about conditions, symptoms, causes, complications, and more: Loaded with medical content, the health bot instance can provide information about medical conditions, symptoms, causes, and complications; for example, "information about diabetes," "what are the causes of malaria," "tell me about the complications of arthritis."
• Find doctor type: The health bot instance can recommend the appropriate type of doctor to treat an illness; for example, "What type of doctor treats diabetes?"

Examples of scenarios that are typically built by customers as extensions using the scenario authoring elements include the following:

• Health plan inquiries: Your health bot instance can be customized to access information about health plan details, such as pricing and benefits.
• Finding providers: Your health bot instance can allow customers to search for doctors by specialty, in-network status, and other specifications.
• Scheduling appointments: Your health bot instance can be designed to allow your customers to schedule appointments easily and securely.

Ref. ~ Microsoft Healthcare Bot technical documentation

Deploying your Custom COVID-19 Healthcare Bot 

Public healthcare providers on the frontline of COVID-19 response have had to act quickly to support the sudden spike in inquiries from patients and constituents looking to get answers to a common set of requests such as, 

• Up-to-date outbreak information, 
• Symptoms 
• Risk factors for people worried about infection
• Suggest a next course of action. 

Many of these providers have expressed concerns with being able to support the volumes of inquiries, and consequently have been using the Microsoft Healthcare Bot to help provide critical information to their patients.

In a nutshell Microsoft’s Healthcare Bot  is a scalable Azure-based SaaS solution that empowers Microsoft customers and partners to build and deploy compliant, AI-powered health agents, allowing them to offer their users intelligent, personalized access to health-related information and interactions through a natural conversation experience. 

It is one solution that uses AI to help the CDC and other frontline organizations to provide help to those who need it.

The Healthcare Bot can easily be customized to suit an organizations scenarios and protocols. 

To assist in the rapid deployment of COVID-19 specific bots Microsoft has made available a set of COVID-19 templates that customers can use and modify:

• COVID-19 Risk Assessment
• COVID-19 Frequently Asked Questions
• COVID-19 Worldwide metrics
• COVID-19 Clinical Triage

To help you deploy your COVID-19 healthcare bot, Microsoft has created a Reference architecture, deployment template.

Reference Architecture

The reference architecture provides guidance on a High Availability deployment of the Healthcare Bot and associated Azure services across 2 regions.

Note: The architecture can also be deployed in a single region, if you choose to deploy in a single region it is recommended that you model and estimate your peak traffic expectations to ensure that a single region deployment is appropriate for your situation.

Alternate Schematic Representation with Workflow:

Note: 

• Unless otherwise noted explicitly, the first region listed in the locations parameter (array) will represent the primary region and the second will denote the secondary region.
• The ARM template parameter name has to be unique for each Health Bot deployment. Use an alpha numberic value for this name parameter. All Azure resources deployed by the ARM template will have names prefixed with this deployment name.
• Azure Traffic Manager is used to shift the Web Chat Client and QnA Maker API traffic across the individual Azure App Service instances deployed in the two regions. The end user (customer) is responsible for configuring the respective traffic routing algorithm in the Traffic Manager to ensure the traffic is split evenly between the App Service instances as per their requirements.

Deployment Template

To assist in deploying the reference architecture Microsoft has developed an ARM template for you to use. The step by step instruction to deploy and configure the reference architecture can be found here: Deploy Microsoft Health Bot Reference Architecture

To then set up your Health Bot – follow the instruction in the Quick Start: Setting Up Your COVID-19 Health Bot

If you are ready to deploy and would like assistance:  

1. Contact your account team for a quick demo and/ or alignment of resources.
2. Speak to one of our Health Bot Partners who can help you deploy and customize your own COVID-19 Health Bot.

Additional Resources:

• Quick Start: Setting Up Your COVID-19 Health Bot
• Video Series – Microsoft Healthcare Bot Service for COVID-19: Getting Started
• Demo Bot (Not COVID-19 Specific).  Click on: Try a demo of an example
• Healthcare Bot Documentation
• Blog: Delivering Information and eliminating bottlenecks with CDC’s COVID-19 assessment bot

Supercomputing Mobilizing against COVID19

Tech has been taking some heavy losses from the coronavirus pandemic. Global supply chains have been disrupted, virtually every major tech conference taking place over the next few months has been canceled, and supercomputer facilities have even begun preemptively restricting visitor access. But tech is striking back, and hard: day by day, more and more organizations are dedicating supercomputing power toward the effort to diagnose, understand and fight back against COVID-19.

Testing for COVID-19

Before supercomputers began spinning up to find a cure, researchers were scrambling to simply diagnose the disease as cases in China’s Hubei province spun out of control.

With limited (and rapidly iterated) test kits available, Chinese researchers turned to AI and supercomputing for answers. They trained an AI model on China’s first petascale supercomputer, Tianhe-1, with the aim of distinguishing between the CT scans of pneumonic patients with COVID-19 and patients with non-COVID-19 pneumonia.

In a paper, the researchers reported nearly 80% accuracy when testing this method against external datasets, dramatically outperforming early test kits as well as human radiologists:





The Summit supercomputer. The big gun was brought out early:

One of the first systems to join the fight was the world’s most powerful publicly-ranked supercomputer: Summit. Oak Ridge National Laboratory (ORNL) pitted Summit’s 148 Linpack petaflops of performance against a crucial “spike” protein on the coronavirus that researchers believe may be key to disabling its ability to infect. Testing how various compounds interact with key virus components can be an extremely time-consuming task, so the researchers – a team from ORNL’s Center for Molecular Biophysics –  were granted a discretionary time allocation on Summit, which allowed them to cycle through 8,000 compounds within a few days.

Using Summit, the research time identified 77 compounds that may be promising candidates for testing by medical researchers. “Summit was needed to rapidly get the simulation results we needed. It took us a day or two whereas it would have taken months on a normal computer,” said Jeremy Smith, director of UT/ORNL CMB and principal researcher for the study. The researchers are preparing to repeat the study using a new, higher-quality model of the spike protein recently made available.

Major organizations have opened their doors – and wallets – for coronavirus computing proposals

Last week, the National Science Foundation (NSF) issued a Dear Colleague Letter expressing interest in proposals for “non-medical, non-clinical-care research that can be used immediately to be understand how to model and understand the spread of COVID-19; to inform and educate about the science of virus transmission and prevention; and to encourage the development of processes and actions to address this global challenge.” Two days later, it issued another Dear Colleague Letter specifically inviting rapid response research proposals for COVID-19 computing activities through its Office of Advanced Cyberinfrastructure. As a complement to existing funding opportunities, the NSF also invited requests for supplemental funding.

Even with their quick response, though, the NSF weren’t the first to open their pocketbooks. In January, the European Commission announced a €10 million call for expressions of interest for projects that fight COVID-19 through vaccine development, treatment and diagnostics. Then, on the same day as the latest NSF Dear Colleague Letter, they announced an additional €37.5 million in funding.

€3 million of this funding has already been allocated to the Exscalate4CoV (E4C) program in Italy – one of the hardest-hit countries. E4C is operating through Exscalate, a supercomputing platform that uses a chemical library of over 500 billion molecules to conduct pathogen research.

Specifically, E4C is aiming to identify candidate molecules for drugs, help design a biochemical and cellular screening test, identify key genomic regions in COVID-19 and more.

Beyond E4C, the EU also highlighted “on-demand, large-scale virtual screening” of potential drugs and antibodies at the HPC Centre of Excellence for Computational Biomolecular Research, as well as “prioritized and immediate access” to supercomputers operated by the EuroHPC Joint Undertaking.

Presumably, as the NSF and European Commission funding opportunities are leveraged, high-performance computing will play an increasingly large role in the fight against the coronavirus.



Post by Jai Krishna Ponnappan

AI app can listen to your cough & detect COVID-19

EPFL researchers have developed an artificial intelligence-based system that can listen to your cough and indicate whether you have COVID-19.

With the new Coughvid app, developed by five researchers at EPFL's Embedded Systems Laboratory (ESL), you can record your cough on a smartphone and find out whether you might have COVID-19. So how can a smartphone app detect the new coronavirus? "According to the World Health Organization, 67.7% of people who have the virus present with a dry cough—producing no mucus—as opposed to the wet cough typical of a cold or allergy," says David Atienza, a professor at EPFL's School of Engineering who is also the head of ESL and a member of the Coughvid development team. The app is still being developed and will be released in the next few weeks.

Free and anonymous

Once the app is available, users will simply need to install it and record their cough—the results will appear immediately. "We wanted to develop a reliable, easy-to-use system that could be deployed for large-scale testing," says Atienza. "It's an alternative to conventional tests." In addition to being easy to use, the app has the advantage of being non-invasive, free and anonymous. "The app has a 70% accuracy rate," he adds. "That said, people who think they may have the disease should still go see their doctor. Coughvid is not a substitute for a medical exam."

Using artificial intelligence to help patients

Coughvid uses artificial intelligence to distinguish between different types of coughs based on their sound. "The idea is not new. Doctors already listen to their patients' coughs to diagnose whooping cough, asthma and pneumonia," says Atienza.

Right now his team is collecting as much data as possible to train the app to distinguish between the coughs of people with COVID-19, healthy people, and people with other kinds of respiratory ailments. "We'll release the app once we've accumulated enough data. It could take a few more weeks," says Atienza. In the meantime, COVID-19 patients who would like to contribute to the development work can record their cough at https://coughvid.epfl.ch/ or on the Coughvid mobile app.

Coronavirus treatment trial uses AI to speed results

The first hospital network in the U.S. has joined an international clinical trial using artificial intelligence to help determine which treatments for patients with the novel coronavirus are most effective on an on-going basis.

Why it matters: In the midst of a pandemic, scientists face dueling needs: to find treatments quickly and to ensure they are safe and effective. By using this new type of adaptive platform, doctors hope to collect clinical data that will help more quickly determine what actually works.

“The solution is to find an optimal trade-off between doing something now, such as prescribing a drug off-label, or waiting until traditional clinical trials are complete.”

— Derek Angus, senior trial investigator and professor at University of Pittsburgh School of Medicine, told a press briefing

State of play: No treatments have been approved for COVID-19 yet. Researchers have made headway in mapping how the virus attaches and infects human cells — helping "guide drug developers, atom by atom, in devising safe and effective ways to treat COVID-19," National Institutes of Health director Francis Collins writes.

• But new drugs take a long time to develop, partly because they must first be tested for safety before broadening to test for safety and efficacy.
• While many companies are working on new treatments, others have focused on testing drugs for other conditions that have already met safety requirements.

What's new: The University of Pittsburgh Medical Center (UPMC) is the first American hospital system to join an international treatment trial called REMAP-COVID19, which is enrolling patients with COVID-19 in North America, Europe, Australia and New Zealand so far.

How it works: Starting Thursday, UPMC's system of 40 hospitals began offering the trial to patients who have moderate to severe complications from COVID-19, Angus said.

• Patients in the trial will receive their current standard of care. About 12.5% will receive placebo at the launch and the rest will be randomly selected to multiple interventions with one or more antibiotics, antivirals, steroids, and medicines that regulate the immune system, including the drug hydroxychloroquine.
• The platform, based on an existing one called REMAP-CAP, is integrated with UPMC's electronic health records and the data collected via a worldwide machine-learning system that continuously determines what combination of therapies is performing best.
• As more data is collected, more patients will be steered toward the therapies doing well, Angus said.
• The adaptive trial format, published Thursday in the journal Annals of the American Thoracic Society, can allow new treatments to be rolled into the trial.

"This idea came to us after the H1N1 [epidemic], when everyone scrambled to do traditional trials" but by the time those were established, the outbreak had moved on, Angus said. "We asked, how we can do this better."

The big picture: There are more than 400 listed clinical trials for treatments, therapies and vaccines related to COVID-19.

• These include trials led by the NIH, which is also testing hydroxychloroquine and the antiviral drug remdesivir.
• Others plan to evaluate HIV drug Kaletra, Mesoblast's stem cell treatment, experimental antibody treatments like SAB-301, monoclonal antibody treatments like REGN3048 and REGN3051, amongst others.

Healthy skepticism of Artificial Intelligence & Coronavirus

The COVID-19 outbreak has spurred considerable news coverage about the ways artificial intelligence (AI) can combat the pandemic’s spread. Unfortunately, much of it has failed to be appropriately skeptical about the claims of AI’s value. Like many tools, AI has a role to play, but its effect on the outbreak is probably small. While this may change in the future, technologies like data reporting, telemedicine, and conventional diagnostic tools are currently far more impactful than AI.

1. LOOK TO THE SUBJECT-MATTER EXPERTS

Still, various news articles have dramatized the role AI is playing in the pandemic by overstating what tasks it can perform, inflating its effectiveness and scale, neglecting the level of human involvement, and being careless in consideration of related risks. In fact, the COVID-19 AI-hype has been diverse enough to cover the greatest hits of exaggerated claims around AI. And so, framed around examples from the COVID-19 outbreak, here are eight considerations for a skeptic’s approach to AI claims.

No matter what the topic, AI is only helpful when applied judiciously by subject-matter experts—people with long-standing experience with the problem that they are trying to solve. Despite all the talk of algorithms and big data, deciding what to predict and how to frame those predictions is frequently the most challenging aspect of applying AI. Effectively predicting a badly defined problem is worse than doing nothing at all. Likewise, it always requires subject matter expertise to know if models will continue to work in the future, be accurate on different populations, and enable meaningful interventions.

In the case of predicting the spread of COVID-19, look to the epidemiologists, who have been using statistical models to examine pandemics for a long time. Simple mathematical models of smallpox mortality date all the way back to 1766, and modern mathematical epidemiology started in the early 1900s. The field has developed extensive knowledge of its particular problems, such as how to consider community factors in the rate of disease transmission, that most computer scientists, statisticians, and machine learning engineers will not have.

“There is no value in AI without subject-matter expertise.”

It is certainly the case that some of the epidemiological models employ AI. However, this should not be confused for AI predicting the spread of COVID-19 on its own. In contrast to AI models that only learn patterns from historical data, epidemiologists are building statistical models that explicitly incorporate a century of scientific discovery. These approaches are very, very different. Journalists that breathlessly cover the “AI that predicted coronavirus” and the quants on Twitter creating their first-ever models of pandemics should take heed: There is no value in AI without subject-matter expertise.

2. AI NEEDS LOTS OF DATA

The set of algorithms that conquered Go, a strategy board game, and “Jeopardy!” have accomplishing impressive feats, but they are still just (very complex) pattern recognition. To learn how to do anything, AI needs tons of prior data with known outcomes. For instance, this might be the database of historical “Jeopardy!” questions, as well as the correct answers. Alternatively, a comprehensive computational simulation can be used to train the model, as is the case for Go and chess. Without one of these two approaches, AI cannot do much of anything. This explains why AI alone can’t predict the spread of new pandemics: There is no database of prior COVID-19 outbreaks (as there is for the flu)

To even attempt this, companies would need to collect extensive thermal imaging data from people while simultaneously taking their temperature with a conventional thermometer. In addition to attaining a sample diverse in age, gender, size, and other factors, this would also require that many of these people actually have fevers—the outcome they are trying to predict. It stretches credibility that, amid a global pandemic, companies are collecting data from significant populations of fevered persons. While there are other potential ways to attain pre-existing datasets, questioning the data sources is always a meaningful way to assess the viability of an AI system.

3. DON’T TRUST AI’S ACCURACY

The company Alibaba claims it can use AI on CT imagery to diagnose COVID-19, and now Bloomberg is reporting that the company is offering this diagnostic software to European countries for free. There is some appeal to the idea. Currently, COVID-19 diagnosis is done through a process called polymerase chain reaction (PCR), which requires specialized equipment. Including shipping time, it can easily take several days, whereas Alibaba says its model is much faster and is 96% accurate.

However, it is not clear that this accuracy number is trustworthy. A poorly kept secret of AI practitioners is that 96% accuracy is suspiciously high for any machine learning problem. If not carefully managed, an AI algorithm will go to extraordinary lengths to find patterns in data that are associated with the outcome it is trying to predict. However, these patterns may be totally nonsensical and only appear to work during development. In fact, an inflated accuracy number can actually be an important sign that an AI model is not going to be effective out in the world. That Alibaba claims its model works that well without caveat or self-criticism is suspicious on its face.

“[A]n inflated accuracy number can actually be an important sign that an AI model is not going to be effective out in the world.”

In addition, accuracy alone does not indicate enough to evaluate the quality of predictions. Imagine if 90% of the people in the training data were healthy, and the remaining 10% had COVID-19. If the model was correctly predicting all of the healthy people, a 96% accuracy could still be true—but the model would still be missing 40% of the infected people. This is why it’s important to also know the model’s “sensitivity,” which is the percent of correct predictions for individuals who have COVID-19 (rather than for everyone). This is especially important when one type of mistaken prediction is worse than the other, which is the case now. It is far worse to mistakenly suggest that a person with COVID-19 is not sick (which might allow them to continue infecting others) than it is to suggest a healthy person has COVID-19.

Broadly, this is a task that seems like it could be done by AI, and it might be. Emerging research suggests that there is promise in this approach, but the debate is unsettled. For now, the American College of Radiology says that “the findings on chest imaging in COVID-19 are not specific, and overlap with other infections,” and that it should not be used as a “first-line test to diagnose COVID-19.” Until stronger evidence is presented and AI models are externally validated, medical providers should not consider changing their diagnostic workflows—especially not during a pandemic.

4. REAL-WORLD DEPLOYMENT DEGRADES AI PERFORMANCE

The circumstances in which an AI system is deployed can also have huge implications for how valuable it really is. When AI models leave development and start making real-world predictions, they nearly always degrade in performance. In evaluating CT scans, a model that can differentiate between healthy people and those with COVID-19 might start to fail when it encounters patients who are sick with the regular flu (and it is still flu season in the United States, after all). A drop of 10% accuracy or more during deployment would not be unusual.

In a recent paper about the diagnosis of malignant moles with AI, researchers noticed that their models had learned that rulers were frequently present in images of moles known to be malignant. So, of course, the model learned that images without rulers were more likely to be benign. This is a learning pattern that leads to the appearance of high accuracy during model development, but it causes a steep drop in performance during the actual application in a health-care setting. This is why independent validation is absolutely essential before using new and high-impact AI systems.

“When AI models leave development and start making real-world predictions, they nearly always degrade in performance.”

This should engender even more skepticism of claims that AI can be used to measure body temperature. Even if a company did invest in creating this dataset, as previously discussed, reality is far more complicated than a lab. While measuring core temperature from thermal body measurements is imperfect even in lab conditions, environmental factors make the problem much harder. The approach requires an infrared camera to get a clear and precise view of the inner face, and it is affected by humidity and the ambient temperature of the target. While it is becoming more effective, the Centers for Disease Control and Prevention still maintain that thermal imaging cannot be used on its own—a second confirmatory test with an accurate thermometer is required.

5. MOST PREDICTIONS MUST ENABLE AN INTERVENTION TO REALLY MATTER

In high-stakes applications of AI, it typically requires a prediction that isn’t just accurate, but also one that meaningfully enables an intervention by a human. This means sufficient trust in the AI system is necessary to take action, which could mean prioritizing health-care based on the CT scans or allocating emergency funding to areas where modeling shows COVID-19 spread.

With thermal imaging for fever-detection, an intervention might imply using these systems to block entry into airports, supermarkets, pharmacies, and public spaces. But evidence shows that as many as 90% of people flagged by thermal imaging can be false positives. In an environment where febrile people know that they are supposed to stay home, this ratio could be much higher. So, while preventing people with fever (and potentially COVID-19) from enabling community transmission is a meaningful goal, there must be a willingness to establish checkpoints and a confirmatory test, or risk constraining significant chunks of the population.

This should be a constant consideration for implementing AI systems, especially those used in governance. For instance, the AI fraud-detection systems used by the IRS and the Centers for Medicare and Medicaid Services do not determine wrongdoing on their own; rather, they prioritize returns and claims for auditing by investigators. Similarly, the celebrated AI model that identifies Chicago homes with lead paint does not itself make the final call, but instead flags the residence for lead paint inspectors.

6. AI IS FAR BETTER AT MINUTE DETAILS THAN BIG, RARE EVENTS

Wired ran a piece in January titled “An AI Epidemiologist Sent the First Warnings of the Wuhan Virus” about a warning issued on Dec. 31 by infectious disease surveillance company, BlueDot. One blog post even said the company predicted the outbreak “before it happened.” However, this isn’t really true. There is reporting that suggests Chinese officials knew about the coronavirus from lab testing as early as Dec. 26. Further, doctors in Wuhan were spreading concerns online (despite Chinese government censorship) and the Program for Monitoring Emerging Diseases, run by human volunteers, put out a notification on Dec. 30.

That said, the approach taken by BlueDot and similar endeavors like HealthMap at Boston Children’s Hospital aren’t unreasonable. Both teams are a mix of data scientists and epidemiologists, and they look across health-care analyses and news articles around the world and in many languages in order to find potential new infectious disease outbreaks. This is a plausible use case for machine learning and natural language processing and is a useful tool to assist human observers. So, the hype, in this case, doesn’t come from skepticism about the feasibility of the application, but rather the specific type of value it brings.

“AI is unlikely to build the contextual understanding to distinguish between a new but manageable outbreak and an emerging pandemic of global proportions.”

Even as these systems improve, AI is unlikely to build the contextual understanding to distinguish between a new but manageable outbreak and an emerging pandemic of global proportions. AI can hardly be blamed. Predicting rare events is just very hard, and AI’s reliance on historical data does it no favors here. However, AI does offer quite a bit of value at the opposite end of the spectrum—providing minute detail.

For example, just last week, California Gov. Gavin Newsom explicitly praised BlueDot’s work to model the spread of the coronavirus to specific zip codes, incorporating flight-pattern data. This enables relatively precise provisioning of funding, supplies, and medical staff based on the level of exposure in each zip code. This reveals one of the great strengths of AI: its ability to quickly make individualized predictions when it would be much harder to do so individually. Of course, individualized predictions require individualized data, which can lead to unintended consequences.

7. THERE WILL BE UNINTENDED CONSEQUENCES

AI implementations tend to have troubling second-order consequences outside of their exact purview. For instance, consolidation of market power, insecure data accumulation, and surveillance concerns are very common byproducts of AI use. In the case of AI for fighting COVID-19, the surveillance issues are pervasive. In South Korea, the neighbors of confirmed COVID-19 patients were given details of that person’s travel and commute history. Taiwan, which in many ways had a proactive response to the coronavirus, used cell phone data to monitor individuals who had been assigned to stay in their homes. Israel and Italy are moving in the same direction. Of exceptional concern is the deployed social control technology in China, which nebulously uses AI to individually approve or deny access to public space.

Government action that curtails civil liberties during an emergency (and likely afterwards) is only part of the problem. The incentives that markets create can also lead to long-term undermining of privacy. At this moment, Clearview AI and Palantir are among the companies pitching mass-scale surveillance tools to the federal government. This is the same Clearview AI that scraped the web to make an enormous (and unethical) database of faces—and it was doing so as a reaction to an existing demand in police departments for identifying suspects with AI-driven facial recognition. If governments and companies continue to signal that they would use invasive systems, ambitious and unscrupulous start-ups will find inventive new ways to collect more data than ever before to meet that demand.

8. DON’T FORGET: AI WILL BE BIASED

In new approaches to using AI in high-stakes circumstances, bias should be a serious concern. Bias in AI models results in skewed estimates across different subgroups, such as women, racial minorities, or people with disabilities. In turn, this frequently leads to discriminatory outcomes, as AI models are often seen as objective and neutral.

While investigative reporting and scientific research has raised awareness about many instances of AI bias, it is important to realize that AI bias is more systemic than anecdotal. An informed AI skeptic should hold the default assumption that AI models are biased, unless proven otherwise.

“An informed AI skeptic should hold the default assumption that AI models are biased, unless proven otherwise.”

For example, a preprint paper suggests it is possible to use biomarkers to predict mortality risk of Wuhan COVID-19 patients. This might then be used to prioritize care for those most at risk—a noble goal. However, there are myriad sources of potential bias in this type of prediction. Biological associations between race, gender, age, and these biomarkers could lead to biased estimates that don’t represent mortality risk. Unmeasured behavioral characteristics can lead to biases, too. It is reasonable to suspect that smoking history, more common among Chinese men and a risk factor for death by COVID-19, could bias the model into broadly overestimating male risk of death.

Especially for models involving humans, there are so many potential sources of bias that they cannot be dismissed without investigation. If an AI model has no documented and evaluated biases, it should increase a skeptic’s certainty that they remain hidden, unresolved, and pernicious.

THE FUTURE OF AI SYSTEMS IS MORE PROMISING

While this article takes a deliberately skeptical perspective, the future impact of AI on many of these applications is bright. For instance, while diagnosis of COVID-19 with CT scans is of questionable value right now, the impact that AI is having on medical imaging is substantial. Emerging applications can evaluate the malignancy of tissue abnormalities, study skeletal structures, and reduce the need for invasive biopsies.

Other applications show great promise, though it is too soon to tell if they will meaningfully impact this pandemic. For instance, AI-designed drugs are just now starting human trials. The use of AI to summarize thousands of research papers may also quicken medical discoveries relevant to COVID-19.

AI is a widely applicable technology, but its advantages need to be hedged in a realistic understanding of its limitations. To that end, the goal of this paper is not to broadly disparage the contributions that AI can make, but instead to encourage a critical and discerning eye for the specific circumstances in which AI can be meaningful.

COVID-19 High Performance Computing Consortium

The COVID-19 High Performance Computing Consortium Bringing together the Federal government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research. Over 402 petaflops, 105,334 nodes, 3,539,044 CPU cores, 41,286 GPUs, and counting.

The world's leading medical researchers are rushing to find a treatment for COVID-19 with the help of the most powerful and advanced supercomputers in the world.

Researchers aross the globe are submitting potential treatments and cures to the COVID-19 High Performance Computing Consortium.

The consortium, using a network of supercomputers and laboratotires, can run through simulations to narrow down or rule out drug compounds to use in a cure much faster than traditional methods.

"It's a means by which one can begin to analyze tremendously complex or large problems," says Vice President of Technical Computing at IBM Cognitive Systems Dave Turek. "Pharmaceutical companies may have billions of compounds that could be potential drugs."

Any researcher can submit proposals to the consortium for the supercomputes to run through.

"So, there are very novel techniques, specifically using A.I. on these supercomputers that are beginnign to speculate about new kinds of molecules that could be created to treat COVID-19," says Turek.

The COVID-19 High Performance Computing Consortium is a unique private-public effort spearheaded by the White House Office of Science and Technology Policy, the U.S. Department of Energy and IBM to bring together federal government, industry, and academic leaders who are volunteering free compute time and resources on their world-class machines.

Consortium partners include:

• Industry
• IBM
• Amazon Web Services
• AMD
• Google Cloud
• Hewlett Packard Enterprise
• Microsoft
• NVIDIA
• Academia
• Massachusetts Institute of Technology
• Rensselaer Polytechnic Institute
• University of Illinois
• University of Texas at Austin
• University of California - San Diego
• Carnegie Mellon University
• University of Pittsburgh
• Indiana University
• University of Wisconsin-Madison
• Department of Energy National Laboratories
• Argonne National Laboratory
• Lawrence Livermore National Laboratory
• Los Alamos National Laboratory
• Oak Ridge National Laboratory
• National Energy Research Scientific Computing Center
• Sandia National Laboratories
• Federal Agencies
• National Science Foundation
• XSEDE
• Pittsburgh Supercomputing Center (PSC)
• Texas Advanced Computing Center (TACC)
• San Diego Supercomputer Center (SDSC)
• National Center for Supercomputing Applications (NCSA)
• Indiana University Pervasive Technology Institute (IUPTI)
• Open Science Grid (OSG)
• National Center for Atmospheric Research (NCAR)
• NASA

Researchers are invited to submit COVID-19 related research proposals to the consortium via this online portal, which will then be reviewed for matching with computing resources from one of the partner institutions. An expert panel comprised of top scientists and computing researchers will work with proposers to assess the public health benefit of the work, with emphasis on projects that can ensure rapid results.

Fighting COVID-19 will require extensive research in areas like bioinformatics, epidemiology, and molecular modeling to understand the threat we’re facing and form strategies to address it. This work demands a massive amount of computational capacity. The COVID-19 High Performance Computing Consortium helps aggregate computing capabilities from the world's most powerful and advanced computers to help COVID-19 researchers execute complex computational research programs to help fight the virus.

About the Consortium, the HPC Systems & How to Join

Consortium members manage a range of computing capabilities that span from small clusters to some of the largest supercomputers in the world. As a member, you would support this crucial work by not only offering your computational resources, but also your deep technical capabilities and expertise to help COVID-19 researchers execute complex computational research programs. We hope that you will join us in this crucial mission.

We are currently providing broad access to portions of over 30 supercomputing systems, representing over over 402 petaflops, 105,334 nodes, 3,539,044 CPU cores, 41,286 GPUs, and counting. Their basic specifications are described below. Additional resources will be added as our consortium grows; please check back for updates.