How AI Healthcare Could Help Avoid the Next Pandemic
By: Kaylie Gyarmathy on July 31, 2020
Artificial intelligence (AI) technology has already had a profound impact on a number of industries and transformed the way companies make decisions. From a pure potential standpoint, however, AI healthcare is perhaps the most promising frontier for AI and machine learning platforms. The COVID-19 pandemic has made the need for AI healthcare more evident than ever, and the rush to combat the virus using every available tool has demonstrated that AI could be essential in the fight against future pandemics.
How Predictive AI Technology Models Pandemics
The first warnings about the COVID-19 pandemic actually came from an AI platform used by Boston Children’s Hospital to scan global information sources for indications of potential disease outbreaks. After identifying reports of several cases of a pneumonia-type illness in Wuhan, China, the system issued a notification that rated the risk as a three out of five. On the same day, a separate monitoring system run by the International Society for Infectious Diseases (ISID) flagged the same reports as a potential pandemic risk.
These systems used big data gathered from sources around the world to provide advance warning of disease outbreaks and track the spread of new cases in real-time ahead of any comprehensive data-sharing efforts between global health organizations. But AI healthcare technology can also be used to model a variety of factors related to a pandemic. For instance, one study used machine learning to model the expected impact that COVID-19 and widespread lockdowns would have on global gasoline demand.
But more critically, AI is being incorporated into existing medical diagnostic systems to identify patterns and trends so that healthcare professionals can deliver treatment more effectively and public health officials can craft better policies to slow the spread. For example, several Chinese researchers have developed AI tools that can predict likely survival rates through blood samples and distinguish COVID-19 from other types of pneumonia on chest CT scans within seconds, both of which relieve pressure on frontline medical personnel and helps them to prioritize and treat patients more rapidly.
Google’s DeepMind has used its new AlphaFold system to analyze the protein structure of the novel coronavirus, which has helped researchers better understand its genetic code and hopefully accelerate the timeline for developing a vaccine. The combination of Internet of Things (IoT) systems and AI software has also allowed medical experts to gather valuable data points that make it easier to model out factors like infection rates, incubation times, and transmission modes. All of this information can be used to inform public health responses and medical treatment.
Using AI to Plan for the "Next" COVID-19
For all the benefits AI has provided in the fight against COVID-19, the unfortunate reality is that the technology hasn’t been used to its fullest potential when it comes to healthcare. Data sharing between countries has proven difficult due to conflicting privacy laws when it comes to data usage, which limited the effectiveness of predictive modeling. Moreover, the healthcare industry is only in the early stages of implementing the AI and machine learning technology that makes personalized prediction possible.
Other industries, such as financial services, media, and retail, have been fundamentally transformed by data tools that allow them to accurately model human behavior and assess risk more accurately. Healthcare data could potentially be used to create better predictive modeling about which areas will most likely be impacted by a disease outbreak and what risk levels individuals face. Data-driven contact tracing models, for instance, could make future statewide lockdowns unnecessary and allow public health officials to manage high-risk populations and areas with much greater precision.
Potential Problems for AI Healthcare
Of course, leaning too heavily upon AI to handle situations it wasn’t designed to account for can also lead to problems. Even now, many companies are finding that the machine learning applications they’ve purchased to streamline their business operations are struggling to adapt to sudden changes in the market and in human behavior. Algorithms that handle tasks like inventory management, fraud detection, and marketing, for instance, simply don’t have enough data to adapt to unprecedented changes and have required some measure of human intervention to continue to function.
Whatever AI solutions are implemented in the future, then, it will be critical for data scientists and software designers to keep a close watch over their performance. Visibility and control will remain important factors in any digital infrastructure that embraces AI-driven automation and predictive modeling.
Implementing AI Healthcare Solutions with a Data Center
Predictive AI and machine learning platforms require access to scalable computing power that’s capable of processing massive amounts of unstructured data. While public cloud services can provide that processing capability as well as proven analytics software applications, few healthcare organizations can afford to entrust their data with a purely cloud-based infrastructure.
Fortunately, colocation data centers offer the healthcare industry an ideal solution when it comes to facilitating an AI-powered digital transformation. Sensitive health data that must comply with HIPAA guidelines can be safely stored on secure servers within the data center environment. Those servers can then be integrated with cloud-based platforms as part of a dynamic hybrid IT environment that can be scaled appropriately when the need arises.
At vXchnge, our colocation services go a step beyond power, cooling, and connectivity thanks to our innovative in\site intelligent monitoring platform. With in\site, our colocation customers can retain the same level of visibility and control over their infrastructure, which allows them to make better business decisions based on real-time data. To learn more about how vXchnge colocation data centers can drive your healthcare organization’s digital transformation, contact our team today.
About Kaylie Gyarmathy
As the Marketing Manager for vXchnge, Kaylie handles the coordination and logistics of tradeshows and events. She is responsible for social media marketing and brand promotion through various outlets. She enjoys developing new ways and events to capture the attention of the vXchnge audience.