Using AI in the Fight Against COVID-19: From Lab to Jab
Machine-learning algorithms and deep-learning systems were indispensable in the creation and distribution of the COVID-19 vaccine, a recent review demonstrates.
When the coronavirus first emerged, researchers raced to develop a vaccine to save lives and end the pandemic. Artificial intelligence (AI) accelerated the process in vaccine development in ways never before seen.
Machine-learning algorithms examined vast quantities of viral genomic data, identifying possible vaccine targets much more quickly than human researchers could.
Two Key AI Approaches
Broadly, two types of AI were used in vaccine development:
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Machine-learning algorithms: Suited for structured data, these algorithms require more human oversight. Machine learning was essential in COVID-19 vaccine development for ‘reverse vaccinology,’ which aims to identify antigens that could provoke an immune response by analyzing a pathogen’s genetic sequences.
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Deep learning: A subset of machine learning, deep learning uses multilayered neural networks to mimic the complex decision-making capabilities of the human brain. It can analyze unstructured data sets. Deep-learning models have been used to generate novel proteins that might act as potential vaccines.
Researchers at the Islamic Azad University, Iran, conducted a detailed review of AI’s “pivotal role” in the global health response to the pandemic.
“AI’s ability to integrate computational speed with biological complexity redefined the boundaries of what is possible in global health responses, signaling a new era of AI-driven therapeutic development for future crises,” the authors said.
Speeding Up Vaccine Development
For vaccines like Pfizer-BioNTech’s and Moderna’s, AI was essential for rapidly analyzing viral genomes to identify how to trigger a strong immune response, which is critical for an effective vaccine.
This led to the identification of the spike protein as the best target for priming our immune systems.
AI-enabled computational models could simulate various molecular configurations of the spike protein, allowing scientists to quickly assess those most likely to trigger an effective immune response.
This ability to model and optimize potential vaccine candidates helped reduce the timeline from concept to clinical trials from years to months.
AI-Powered Clinical Trials
Clinical trials also utilized AI to improve the sorting of participants based on their risk factors, such as age, pre-existing conditions, and location. This allowed researchers to recruit participants more efficiently, focusing on high-risk populations most likely to benefit from the vaccine.
The researchers found that using AI improved the accuracy of trial outcomes and ensured that trials “reflected the diverse populations most affected by the pandemic.”
During clinical trials, AstraZeneca used AI-powered systems to monitor participant data in real-time, which allowed for rapid identification of anomalies or potential side effects. This enabled researchers to quickly adjust trial protocols when necessary.
Addressing Logistical Challenges
The research team also documented how AI assisted in easing vaccine production challenges. Deep-learning algorithms optimized manufacturing by simulating various production scenarios.
These algorithms analyzed numerous factors, including the availability of raw materials, production schedules, and cold storage capacities. They could predict bottlenecks and provide real-time solutions, which helped to reduce possible supply chain disruptions.
AI algorithms were also essential for maintaining the integrity of temperature-sensitive vaccines. Real-time monitoring systems tracked temperature conditions throughout the supply chain, allowing logistical teams to enforce the strict cold chain requirements for mRNA vaccines like Pfizer-BioNTech and Moderna.
There is abundant data supporting AI’s promise in vaccine development and roll-out, and it provides solid evidence that AI has already been pivotal in responding to COVID-19.
The authors cautioned that while AI has many uses in this area, maximizing its potential requires “ensuring the availability of high-quality data, mitigating algorithmic biases and establishing ethical frameworks that prioritize transparency and equity in AI-driven healthcare solutions.”
