To reduce and track the spread of COVID-19, researchers and provider organizations have increasingly turned to artificial intelligence and machine learning tools to improve their surveillance efforts.

For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media.

From predicting patient outcomes to anticipating future hotspots across the country, big data analytics systems have helped health leaders stay ahead of the pandemic, resulting in more efficient care delivery.

However, healthcare organizations’ level of pandemic preparation is only as good as the data available to them. While the industry is no stranger to data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery.

The novel, global nature of the virus has led to significant gaps in COVID-19 data, with inconsistencies in information leaving officials unsure of the effectiveness of public health interventions.

“It’s now well-known that asymptomatic infections are a common phenomenon in the spread of coronavirus. And it’s very important to understand that phenomenon because, depending on how many asymptomatic infections there are, public health interventions might be different,” Lucy Li, PhD, data scientist at the Chan Zuckerberg Biohub, told HealthITAnalytics.

Researchers at the Chan Zuckerberg Biohub are working to overcome this challenge. Using machine learning and cloud computing technology, Li estimated the number of undetected infections at 12 locations in Asia, Europe, and the US over the course of the pandemic.

The results showed that a wide range of infections were undetected in these locations, with the rate of undetected infections as high as over 90 percent in Shanghai.

Additionally, when the virus was first transmitted to these 12 locations, over 98 percent of infections were undetected during the first few weeks of the outbreak. This suggests that the pandemic was already well underway by the time intense testing began to occur.

These findings have important implications for public health policy and provider organizations, Li noted.

“For disease outbreaks where you can detect every single infection, rapid testing and just a small amount of contact tracing is enough to get the epidemic under control. But for coronavirus, because there are so many asymptomatic infections out there, testing alone won’t help control the pandemic,” she said.

“Because usually when you do testing, you’re testing symptomatic patients. But that’s only a subset of the total number of infections out there. You’re really missing a lot of people who are able to spread the infection, but are not quarantining. Being able to get a sense of what that number might be is helpful for allocating resources.”

Li’s research was supported by the AWS Diagnostic Development Initiative, a global effort to accelerate diagnostic research and innovation during the COVID-19 pandemic and to help mitigate future disease outbreaks.

The initiative allows individuals to take advantage of the cloud and other innovative tools, something that Li said was essential for her research.

“The data I’m using are the viral genomes – the viral DNA. As the viral genomes spread through the population, they accumulate mutations. Generally, these mutations are not good or bad, they’re just changes in the genome. Every time the virus is spread to a new person, it could accumulate new mutations. So, if we know how quickly the virus mutates, we can infer how many missing transmission links there were in between the observed genomes,” she said.

“That’s the data I’m fitting the models to. And because there are many different scenarios that could explain what we see in the viral genomes, I have to leverage machine learning and cloud computing to test all of those hypotheses and to see which one can explain the observed changes in the viral genomes.”

These data analytics tools are well-suited to meeting the challenges brought on by COVID-19, Li pointed out.

“In order to try to quantify the unreported infections, we formulate models of how disease spreads in the population. And then we generate many simulations from these models, and we find out which of those simulations fits the data that we see,” she said.

“That allows us to test different levels of under-reporting and understand which of those can best explain the data that we see. That’s not really possible without a lot of computational resources, and it’s a very time-intensive process. The machine learning tool allows us to explore different explanations of the data that we’re seeing, and we can test many hypotheses. It’s a crucial tool for this type of analysis.”

With machine learning and cloud computing technologies, Li was able to streamline a previously time-consuming task.

“Before cloud computing became more common and these big computational resources became available, some of these analyses could take months to run. I’ve seen papers that were based on months of running a very complex model,” Li said.

“But by having access to more computational resources in the cloud, we can shorten that time from months to days, because we’re able to leverage much more memory and better parallelize our analysis.”

The research could help public health officials monitor the rate of under-reporting in real-time, which could indicate how well current surveillance systems are operating.

“The better the current public health surveillance system is at detecting infections, the fewer underreported cases we would have. But if we see the underreported cases increasing, that would suggest that there needs to be more testing in the population. The results of this research can help the public health department determine how much more testing they would need,” Li said.

“This type of research can also help indicate how close we are to the end of the pandemic. By tracking how many people in the population have been infected by the virus or the number of undetected cases, we could get a sense of how far are we from eliminating this disease.”

With the amount of information generated by the COVID-19 pandemic, analytics tools are critical for uncovering new insights and potential solutions.

“Since the start of the pandemic, we’ve racked our brains to figure out what we can do to help the public health departments in reducing the spread of COVID-19. The number one request that we get from public health departments is information. And sometimes, just presenting the raw data to these departments is sufficient by itself,” Li concluded.

“But quite often, we need to use machine learning and mathematical models to infer these parameters or numbers that we can’t directly see in the data. There has been so much effort from different research groups around the world in developing new models to help us tease out the underlying information that’s not obvious from the data alone.”

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Researchers at the University of Iowa (UI) have received a $1 million grant from the National Science Foundation (NSF) to develop a machine learning platform to train algorithms with data from around the world.

The phase one grant will enable the UI team to lead a multi-university and industry collaboration and address concerns around patient privacy and data security in clinical AI development.

The researchers noted that although the use of AI is widespread in healthcare, training effective machine learning algorithms require thousands of samples annotated by doctors. This can lead to privacy and security issues, the team stated.

“Traditional methods of machine learning require a centralized database where patient data can be directly accessed for training a machine learning model,” said Stephen Baek, assistant professor of industrial and systems engineering at UI.

“Such methods are impacted by practical issues such as patient privacy, information security, data ownership, and the burden on hospitals which must create and maintain these centralized databases.”

The team will develop a decentralized, asynchronous solution called ImagiQ, which relies on an ecosystem of machine learning models so that institutions can select models that work best for their populations. Organizations will be able to upload and share the models, not patient data, with each other.

As each institution improves the model using their local patient data sets, models will be uploaded back to a centralized server. This ensemble learning approach will allow the most reliable and efficient models to come to the forefront, resulting in a better AI system for analyzing images like lung x-rays or CT scans that detect tumors.

The UI-led team includes researchers from Stanford University, the University of Chicago, Harvard University, Yale University, and Seoul National University.

Over the next nine months, the group will aim to develop a prototype of the system as well as participate in the Accelerator’s innovation curriculum to ensure the solution has societal impact. By the end of phase one, the team will participate in a pitch competition and proposal evaluation and if selected will proceed to phase two, with potential funding up to $5 million for 24 months.

“ImagiQ will further federated learning by decentralizing the model updates and eliminating the synchronous update cycle,” said Baek. “We are going to create a whole ecosystem of machine learning models that will evolve and improve over time. High performing models will be selected by many institutions, while others are phased out, producing more reliable and trustworthy outputs.”

The research team is part of the AI-driven data and model sharing track topic under the 2020 cohort NSF Convergence Accelerator program, designed to leverage a convergence approach to transition basic research and discovery into practice. NSF is investing more than $27 million to support the teams in phase one to develop the solution groundwork for AI-Driven Data and Model Sharing.

The Convergent Accelerator’s AI-Driven Innovation via Data and Model Sharing topic involves 18 teams concentrating on solution development. These research teams will also address a variety of data and model-related challenges and data types to include platform development to enable easy and efficient data matching and sharing.

“The quantum technology and AI-driven data and model sharing topics were chosen based on community input and identified federal research and development priorities,” said Douglas Maughan, head of the NSF Convergence Accelerator program. “This is the program’s second cohort and we are excited for these teams to use convergence research and innovation-centric fundamentals to accelerate solutions that have a positive societal impact.”

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A deep learning tool could improve genomic sequencing processes, identifying disease-causing mechanisms that might otherwise be missed by traditional screening methods, according to a study published in Nature Machine Intelligence.

Researchers from Children’s Hospital of Philadelphia (CHOP) and New Jersey Institute of Technology (NJIT) developed the tool, which can help predict sites of DNA methylation – a process that can change the activity of DNA without changing its overall structure.

DNA methylation is involved in many key cellular processes and is an important component in gene expression. Errors in methylation can be linked to a wide range of human diseases. Genomic sequencing tools can effectively pinpoint polymorphisms that may cause a disease, but these same methods are unable to capture the effects of methylation because the individual genes still look the same.

Researchers have made a considerable effort to study DNA methylation of N⁶-adenine (6mA) in eukaryotic cells, which include human cells. Although there is genomic data available, the role of methylation in these cells remains elusive.

“Previously, methods that had been developed to identify these methylation sites in the genome were very conservative and could only look at certain nucleotide lengths at a given time, so a large number of methylation sites were missed,” said Hakon Hakonarson, PhD, Director of the Center for Applied Genomics (CAG) at CHOP and one of the senior co-authors of the study.

“We needed to develop a better way of identifying and predicting methylation sites with a tool that could identify these motifs throughout the genome that may have a robust functional impact and are potentially disease causing.”

To overcome this issue, the team developed a deep learning algorithm that could predict where these sites of methylation happened, which could then help researchers determine the effect they might have on nearby genes.

The software, called Deep6mA, applies neural networks to study DNA methylation sites on natural multicellular organisms. This new method holds several advantages, researchers noted. The approach allows for the automation of the sequence feature representation of different levels of detail. Additionally, the method facilitates the integration of a broad spectrum of methylation sequences on nearby genes of interest.

The innovative process could also lead to model development and prediction in large-scale genomic data.

The researchers applied the algorithm to three different types of representative organisms, including A. thaliana, D. melanogaster, and E.coli, the first two being eukaryotic. The deep learning tool was able to identify 6mA methylation sites down to the resolution of a single nucleotide, or basic unit of DNA. Even in this initial confirmation study, researchers were able to visualize regulatory patterns they were unable to see using traditional methods.

“One limitation is that our proposed prediction is purely based on sequence information,” said Zhi Wei, PhD, a professor of computer science at NJIT and a senior co-author of the study.

“Whether a candidate is a 6mA site or not will also depend on many other factors. Methylation, including 6mA, is a dynamic process, which will change with cellular context. In the future, we would like to take other factors into consideration such as gene expression. We hope to predict 6mA across cellular context by integrating other data.”

Despite this limitation, the researchers believe that their study shows the ability for deep learning to accelerate personalized medicine and enhance clinical care.

“We already know that a number of genes have a disease-causing mechanism brought about by methylation, and while this study was not done in human cells, the eukaryotic cell models were very comparable,” Hakonarson said.

“Genomic scientists looking to translate their findings into clinical applications would find this tool very useful, and the level of precision could eventually lead to the discovery of specific cells or targets that are candidates for therapeutic intervention.”

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June 30, 2020 – An artificial intelligence algorithm can analyze electroencephalograph (EEG) electrodes to detect a seizure and accurately pinpoint its location, according to a study published in Scientific Reports.

The researchers stated that epilepsy is one of the most common central nervous system disorders, with nearly four percent of people across different ages diagnosed with epilepsy during their lifetimes.

The current understanding of most seizures is that they occur when normal brain activity is interrupted by a strong, sudden hyper-synchronized firing of a cluster of neurons. During a seizure, if a person is hooked up to an EEG – a device that measures electrical output – the abnormal brain activity is presented as amplified spike-and-wave discharges.

However, when temporal EEG signals are used, it can be difficult to accurately detect a seizure. Researchers developed a network inference technique that would facilitate detection of a seizure and pinpoint its location with improved accuracy.

During an EEG session, a person has electrodes attached to different spots on her head, and each electrode records electrical activity around that spot.

“We treated EEG electrodes as nodes of a network. Using the recordings (time-series data) from each node, we developed a data-driven approach to infer time-varying connections in the network or relationships between nodes,” said Walter Bomela, a postdoctoral fellow in the Preston M. Green Department of Electrical & Systems Engineering at the University of Texas at Arlington. “We want to infer how a brain region is interacting with others.”

These relationships form a network. Once researchers had a network, they could measure its parameters holistically. For example, instead of measuring the strength of a single signal, the team could evaluate the overall network for strength.

One parameter, the Fiedler eigenvalue, starts to increase when a seizure occurs. In network theory, the Fiedler eigenvalue is also related to a network’s synchronicity – the bigger the value the more the network is synchronous.

“This agrees with the theory that during seizure, the brain activity is synchronized,” Bomela said.

A bias toward synchronization also helps to reduce artifact and background noise, researchers noted. For instance, if a person scratches their arm, the associated brain activity will be captured on some EEG channels or electrodes. However, it won’t be synchronized with seizure activity. This structure inherently eliminates the importance of unrelated signals, so that only brain activities that are in sync will significantly increase the Fiedler eigenvalue.

“Our technique allows us to get raw data, process it and extract a feature that’s more informative for the machine learning model to use,” said Bomela. “The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with fewer computing resources.”

Currently, the system works for an individual patient. The next step is to integrate machine learning to generalize the technique for identifying different types of seizures across patients. Researchers are seeking to take advantage of various parameters characterizing the network and use them as features to train the machine learning algorithm.

“The network is like a face,” said Bomela. “You can extract different parameters from an individual’s network — such as the clustering coefficient or closeness centrality — to help machine learning differentiate between different seizures.”

In network theory, similarities in specific parameters are associated with specific networks. In this case, those networks will correspond to different types of seizures.

The team’s overall aim is to one day design a device for people with epilepsy that is analogous to an insulin pump. As the neurons begin to synchronize, the device will provide medication or electrical interference to stop the seizure. However, in order for this to happen, researchers need a better understanding of the neural network.

“While the ultimate goal is to refine the technique for clinical use, right now we are focused on developing methods to identify seizures as drastic changes in brain activity,” said Jr-Shin Li, professor in the Preston M. Green Department of Electrical & Systems Engineering. “These changes are captured by treating the brain as a network in our current method.”

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February 26, 2020 – The Department of Defense (DoD) has adopted a series of ethical principles for the use of artificial intelligence following recommendations made by the Defense Innovation Board last October.

The recommendations came after 15 months of discussion among leading AI experts in commercial industry, government, academia, and the American public, which resulted in thorough feedback and analysis about the use of AI in multiple areas. DoD’s adoption of ethical AI principles aligns with the department’s AI strategy objective directing the US military lead in AI ethics and the lawful use of AI systems.

“The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order,” said Secretary Esper.

“AI technology will change much about the battlefield of the future, but nothing will change America’s steadfast commitment to responsible and lawful behavior. The adoption of AI ethical principles will enhance the department’s commitment to upholding the highest ethical standards as outlined in the DoD AI Strategy, while embracing the US military’s strong history of applying rigorous testing and fielding standards for technology innovations.”

The DoD’s AI ethical principles will build on the US military’s existing ethics framework, which provides a technology-neutral and enduring foundation for ethical behavior. However, the use of AI raises new ambiguities and risks, and the new principles seek to address these challenges and ensure the responsible use of the technology across the department.

The principles will apply to both combat and non-combat functions and will help the US military uphold legal, ethical, and policy commitments in the field of AI.

DoD’s ethical principles include several major areas: Responsibility, which ensures that DoD personnel exercise appropriate levels of judgment and care while maintaining responsibility for the development and use of AI; equitability, which will confirm that the department will take steps to reduce unintended bias in AI algorithms; and traceability, which will ensure that the relevant personnel have an appropriate understanding of AI capabilities.

Additionally, the principles will ensure that the department’s AI capabilities will have explicit, well-defined uses, and that AI is designed to fulfill intended functions while detecting and avoiding unintended consequences.

“Secretary Esper’s leadership on AI and his decision to issue AI Principles for the Department demonstrates not only to DoD, but to countries around the world, that the US and DoD are committed to ethics, and will play a leadership role in ensuring democracies adopt emerging technology responsibly,” said Dr. Eric Schmidt, Chair, Defense Innovation Board.

The DoD Joint Artificial Intelligence Center (JAIC) will coordinate the implementation of ethical AI principles for the department. The JAIC currently leads a series of working groups that solicit input from services and AI technology experts throughout DoD.

“We are grateful to the Defense Innovation Board for their thorough and insightful recommendations that led to the adoption of DoD AI ethical principles,” said Hon. Dana Deasy, DOD Chief Information Officer.

“Ethics remain at the forefront of everything the department does with AI technology, and our teams will use these principles to guide the testing, fielding and scaling of AI-enabled capabilities across the DOD.”

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December 11, 2019 – Twenty-two percent of healthcare organizations use a software platform that provides artificial intelligence capability, according to a recent report from HealthLeaders Media.

This is an eight-point increase from 2017, the organization noted, indicating that AI use is steadily rising among health systems.

Thirty-one percent said they plan to have AI capability within the next three years, and 63 percent said their organizations plan to increase their investments in analytics technologies within the next three years.

HealthLeaders surveyed 128 individuals representing different healthcare provider organizations, aiming to measure AI and analytics use across the healthcare ecosystem.

Providers see a wide range of applications for AI, with 81 percent of respondents saying their organizations currently or plan to apply the technology to clinical data, 72 percent to financial data, and 59 percent to patient data.

Organizations also see potential in analytics capabilities. Sixty-three percent of respondents said they plan to increase their investments in analytics, while 35 percent said their investments would stay the same. Just two percent of respondents said their investments would decrease.

Health systems are also leveraging analytics strategies to extract insights from various sources of information. The report found that 78 percent of respondents said they use descriptive analytics for financial data, while 81 percent said they leverage descriptive analytics for their clinical data.

However, fewer participants use predictive capabilities for financial and clinical data analytics. Sixty-four percent said they apply predictive analytics to their financial data and 52 percent reported using predictive capabilities for clinical data.

Analytics investments have led to a strong return on investment for organizations: Forty-one percent describe their organizations’ return on investment on analytics as acceptable, while 30 percent describe analytics ROI as good and 14 percent describe it as very good.

Just 16 percent of respondents describe their analytics ROI as poor or very poor.

When asked about the most significant challenges in performing analytics, most respondents named issues involving human skills and staffing.

Forty-eight percent of participants see the need for timely analysis as the top challenge in performing analytics over the next three years. Forty-six percent said overcoming insufficient analytics skills would be the most challenging, while thirty-seven percent view insufficient funding as their biggest issue.

In addition to revealing staff education and training issues, these barriers also reflect investment challenges, researchers noted.

“Two of the top three tactical challenges are either indirectly or directly related to financial resources,” the report stated.

“For example, the solution to solving the problem of insufficient skills in analytics is investment in training or adding analytics staff, and insufficient funding in light of other priorities needs no explanation.”

These findings mirror statements from a February 2019 Kaufman Hall report, which said that financial executives should expand the scope of their responsibilities and investments in order to keep up with new technologies.

“Given the demands of the changing business environment, healthcare CFOs nationwide should be critically examining the role they and their finance teams play in their organizations. A singular focus on directing or managing financial operations and the related control/monitoring function is not sufficient going forward,” the report concluded.

“Finance executives must be integral to the development, execution, and monitoring of the organization’s vision and strategy, and be armed with the full breadth of data and analytics required for performance management in healthcare.”

Organizations also cited data-related challenges in performing analytics. Half of respondents said integrating internal clinical and financial data is a top challenge, and 46 percent said integrating external clinical and financial data is a major barrier.

The third most-cited challenge was improving EHR interoperability, with 43 percent of respondents naming this as a major barrier to building analytics capabilities.

Looking ahead, 62 percent of respondents said that the most promising area of analytics development would be clinical best practices, while 54 percent cited real-time delivery of actionable information as the most promising area of analytics.

This indicates that most health systems are interested in using analytics technologies to enhance care delivery and outcomes. Thirty-nine percent of participants also named improved quality of care as the primary goal they would expect to achieve through integrating financial and clinical data.

In September 2019, a survey from HIMSS Analytics and Dimensional Insight yielded similar results, showing that the future of the industry will focus on quality rather than quantity.

“As healthcare organizations move to value-based payment models, they are finding that focusing on clinical metrics, including readmission rates, infection control, and patient outcome improvements is critical for success,” George Dealy, vice president of healthcare solutions at Dimensional Insight, said at the time. 

“Analytics provides tremendous insight into these areas and can benefit healthcare organizations that are navigating this transition.”

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November 26, 2019 – As the foundation of nearly every healthcare trend, process, and solution, data has a vitally important role to play in care delivery and success.

From risk stratification to chronic disease management, precision medicine and medical research, data is at the center of everyday healthcare tasks and broader industry improvements, making it an incredibly valuable resource for organizations.

“If you talk to any data scientist, they’ll tell you that the more quality, scientifically validated data they have, the more likely they’re going to be able to generate useful trends and insights,” said Todd Frech, CIO at Press Ganey.

“The core of everything we do is taking the vast amounts of data that we collect and creating value for hospitals that are trying to improve their operations.”

With healthcare quickly becoming a digital industry, more and more entities are gathering meaning from this big data using artificial intelligence and other advanced analytics technologies.

“The challenge that we’re trying to overcome is that we have more data than a human can process, and we’re trying to develop insights based on those volumes of data. This issue is a natural fit for AI, so the use of this technology is going to continue to accelerate,” Frech said.

“AI can augment humans’ understanding of data, not only from the perspective of generating new insights, but also in generating those insights faster than a typical human analyst processes.”

There are countless examples of AI outperforming humans in analyzing and extracting insights from clinical data. The technology’s potential to transform the industry has led to concerns about robots encroaching on healthcare jobs, creating an environment run entirely by machines and devoid of human interaction.

While AI may disrupt standard care delivery, it’s unlikely that advanced analytics tools will completely take over the role of clinicians. In a field where high-stakes situations and sensitive data are routine, technology can’t simply be left to operate on its own, Frech stressed.

“AI is going to play a bigger part in healthcare, and humans will also continue to play a big part,” he said.

“We can’t just assume that AI is making the right decisions without human validation. There’s a trend that you’re going to see more – what’s called AI augmentation, or human augmentation with AI, more than what you would call complete robotic AI, meaning that you’re letting the AI make decisions without human intervention.”

Recent research has demonstrated that when implementing AI tools, human intervention can lead to optimal results. A study conducted by a team at NYU School of Medicine and the NYU Center for Data Science showed that combining AI with analysis from human radiologists significantly improved breast cancer detection.

Using an AI augmentation approach could also help organizations analyze and measure unstructured data.

“We collect hundreds of thousands of survey data points in the forms of responses to questions, as well as unstructured data in the form of comments. We use AI to look at the comments that come in our surveys. Those comments are obviously in the form of unstructured text, and they convey information on perception of the providers and of the service,” explained Frech.

“Those aren’t yes or no questions. Those are questions that require some soft skills to interpret. We can use AI to do an initial sentiment analysis, and that provides a way for us to really measure this type of data, which is not as binary as some of the data we typically evaluate.”

However, data-driven technologies can’t improve care if they’re fed inaccurate or incomplete information – in fact, this could have the opposite effect.

“Never underestimate the importance of data quality,” said Frech.  “No AI tool is going to work well without high-quality data. People talk about data lakes and unstructured data, and these things are great tools. But without quality data, you’re going to have more of a data swamp than a data lake.”

“If you’re trying to use AI to gather insights without high-quality data, obviously the results aren’t going to pan out. Or even worse, the results could potentially offer dangerous recommendations that could negatively impact people,” he added.

Having a solid data ecosystem ensures that any innovative tools will contribute positively to a health system’s operations, Frech said, as well as communicating openly with other organizations.

“When implementing artificial intelligence or any new technology, make sure that the foundation is strong. Make sure that there’s testing and validation. If that doesn’t happen, there is potential for organizations to take steps backward rather than forward,” he said.

“Find opportunities with your peers, find case studies, talk to people who are using the technology. The more that your organizations can collaborate and learn from each other, the more ideas and successes will increase.”

AI has massive potential to revolutionize the way providers deliver care and make treatment decisions. The road to industry-wide adoption won’t be without its challenges, but the technology will likely make its way into regular clinical care.

“There are a lot of different ways to use AI, and there has been a lot of experimentation. Over time, there will be more and more successes, and those successes will come in fits and starts, depending on how the data in the market mature. There’s too much investment in AI right now to not have some of those successes come into play,” Frech concluded.

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Artificial intelligence, machine learning, natural language processing, and other advanced analytics technologies are driving the growth of some of the most forward-looking  and admired organizations, from Amazon to Netflix to GE. This leading-edge of analytics combines big data and near real-time processing with other advanced tools to deliver the insights you need, before an analyst might have even formulated the right question to ask.

Some early use cases that have gotten a lot of attention include autonomous vehicles (self-driving cars), customer service chatbots, and recommendation engines. These technologies can also diagnose health conditions from medical imaging, advance precision medicine,  and improve the outcomes for cardiac patients.

Benefits of such technologies include improved efficiency, reduced costs, fewer errors, and faster decision-making. These technologies can automate many tasks that used to be performed by humans — from minimum wage customer service representatives, to highly trained and highly paid physicians. That always leads to the question of whether the rise of these technologies will disrupt the workforce as we know it and lead to job losses in the decades ahead.

Some of the top thinkers who have access to information about the current state of AI advances — people like Elon Musk — have also raised concerns about whether AI should be held back lest we lose control of rogue machines that end up doing harm rather than helping.

There are still more questions than answers for how AI and machine learning will ultimately affect society as a whole.

There are so many facets to the conversation about these advanced analytics technologies: practical discussions about techniques, and big picture ethics and strategy debates about where these technologies will take organizations and society in the years ahead. To help you keep up with it all, we’ve assembled a list of some key people to follow on Twitter if you are interested in AI and machine learning. Some of these names may be familiar. Others may be new to you. But all are influential thinkers or practitioners in these advanced analytics fields, and people who you should be following if you care about the future. Are there any who we missed? Please add them in the comments.

Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG’s Infoworld, Ziff Davis Enterprise’s eWeek and Channel Insider, and Penton Technology’s MSPmentor. She’s passionate about the practical use of business intelligence.

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