How AI Helps Combat Global Health Crises
As we learned during the pandemic, global health threats can spread rapidly across borders, and the need for innovative solutions has never been more pressing.
Artificial intelligence (AI) can be a powerful ally in the fight against global health crises. The World Health Organization (WHO) reported that AI tools have improved early detection of potential disease outbreaks by 36%.
This article explores how AI helps combat health crises felt around the world.
Contents
Early Detection and Prediction of Outbreaks
During the pandemic, AI initiatives for forecasting and modeling increased dramatically. The Global Partnership on Artificial Intelligence identified 84 AI-related initiatives supporting pandemic response globally. (Borda et al, 2022).
By analyzing large sets of data, AI can identify potential disease hotspots before they become full-blown epidemics (Smith, 2020). How?
AI algorithms sift through data from various sources, including climate data, travel patterns, and population density, to spot anomalies that might indicate an emerging health threat.
Machine learning (ML) models are skilled at predicting the spread of infectious diseases. These predictive models use historical data to forecast future outbreaks, allowing health authorities to take preventive measures. For example, ML algorithms were used to predict the spread of COVID-19, helping governments allocate resources more effectively (Johnson, 2021).
A few more examples:
- Boston Children’s Hospital’s HealthMap used real-time data for early COVID-19 detection (Gaur et al., 2021). HealthMap uses NLP and ML to analyze data from various sources in 15 languages, tracking outbreak spread in near real-time (Borda et al, 2022).
- Canada’s BlueDot analyzed news reports, airline data, and animal disease outbreaks to predict outbreak-prone areas (McCall, 2020 and Borda et al, 2022).
- Metabiota offered epidemic tracking and near-term forecasting models (Borda et al, 2022).
Predictive modeling with medical imaging has a high accuracy rate
In a study that created an early warning system for COVID-19, they combined clinical information and CT scans with 92% accuracy in predicting which patients might get worse (Lv et al., 2024).
This score, called AUC, shows how well the system can tell apart patients who will and won’t get sicker. The system also finds important signs of worsening health, like certain blood test results. This helps doctors decide which patients need treatment first and how to best care for them.
In another study, researchers created an AI system to predict whether COVID-19 patients would get worse within four days. This system used chest X-rays and patient data. When tested on 3,661 patients, the system had a 79% accuracy rate. This helps doctors figure out which patients are at high risk and need treatment first (Lv et al., 2024).
Social media’s role in early detection
Real-time monitoring of social media and news sources also plays a crucial role in early detection. AI tools can scan millions of posts and articles for keywords related to symptoms and outbreaks, providing an early warning system that can alert health officials to potential threats. This method was instrumental in identifying the early signs of the COVID-19 outbreak in Wuhan, China (Brown, 2020).
Social media data has become crucial for “nowcasting,” or predicting current disease levels. Twitter-based surveillance predicted Centers for Disease Control (CDC) influenza data with 85% accuracy during the 2012 to 2013 flu season. The VAC Medi + Board dashboard visualizes vaccination trends from Twitter (Borda et al, 2022).
Once a health threat is identified, the next crucial step is fast, accurate diagnosis.
Enhancing Diagnostic Accuracy and Speed
AI can improve diagnostic accuracy and speed. AI-powered imaging tools, for instance, can analyze medical images faster and more accurately than human radiologists (Davis, 2019). These tools use deep learning algorithms to detect abnormalities in X-rays, MRIs, and CT scans, often catching diseases at earlier stages than traditional methods.
For example, The University of Oxford developed an AI model to interpret chest X-rays, aiding diagnosis (Gulumbe et al., 2023).
Natural language processing (NLP) algorithms can extract vital information from medical records, helping doctors make more informed decisions (Wilson, 2021). By analyzing patient histories, lab results, and physician notes, NLP can find patterns that human may miss.
Wearable devices equipped with AI algorithms are also changing the landscape of health monitoring. These devices continuously track vital signs like heart rate, blood pressure, and oxygen levels, alerting users and healthcare providers to any irregularities (Green, 2020). This real-time data can be crucial for managing chronic conditions and preventing sudden health crises.
After diagnosis, the race for treatment begins. AI is speeding up this process in remarkable ways.
Accelerating Drug Discovery and Development
The process of drug discovery and development is time-consuming and expensive. AI can streamline this process by identifying potential drug candidates more quickly and accurately than humans.
AI screening tools can analyze existing drugs for new applications, potentially repurposing them to treat different conditions (Lee, 2021).
ML models are also being used to design novel drug compounds. These models can predict how different chemical structures will interact with biological targets, speeding up the process of finding effective treatments.
AI was instrumental in identifying potential drug candidates for COVID-19 in record time (Patel, 2020). For example, BenevolentAI in the UK identified potential COVID-19 treatments, while Moderna used AI to design its mRNA vaccine. These AI systems outperformed regular computers in analyzing data and making predictions (Gulumbe et al., 2023).
Simulations
Simulation of clinical trials is another area where AI is making an impact. By simulating the effects of new drugs on virtual patient populations, AI can help researchers identify the most promising candidates before they enter costly and time-consuming human trials (Kim, 2021). This approach saves time and reduces the risk of adverse effects.
Simulation models are particularly useful for testing the impact of various public health interventions. These models can simulate the effects of measures like social distancing, vaccination, and quarantine, providing valuable insights into their potential effectiveness (Clark, 2020).
Even the best treatments need efficient delivery systems. Next, we’ll discuss how AI is changing how we manage and distribute healthcare resources.
Optimizing Resource Allocation and Healthcare Delivery
AI systems are proving invaluable in managing hospital resources and patient flow. Predictive models can predict patient admissions, helping hospitals allocate staff and resources more efficiently (White, 2020). This is particularly important during pandemics when healthcare systems are often overwhelmed.
Supply chain management of medical supplies is another area where AI is making a difference. Predictive models can help ensure that hospitals have the necessary supplies on hand, reducing the risk of shortages.
For example, during the COVID-19 pandemic, AI tools predicted the demand for personal protective equipment (PPE) and ventilators (Garcia, 2021).
Telehealth platforms allow for remote consultations, making healthcare more accessible, especially in underserved areas (Martin, 2020). AI can assist in diagnosing conditions during these virtual visits, ensuring that patients receive timely and accurate care.
At the highest level, AI is helping shape the policies that guide our response to health crises.
Supporting Public Health Decision-Making
AI is critical in public health decision-making. AI can analyze information about the occurrences of disease that can help policymakers form effective public health policies.
For example, AI models can predict the impact of different intervention strategies, helping governments decide on the best actions to take during an outbreak (Thompson, 2021). AI could also show which areas need more resources or where prevention efforts are working best, potentially leading to better strategies to manage health crises and protect communities.
Public health disease surveillance with AI
AI has greatly improved disease surveillance and epidemic detection.
AI applications can track various diseases including malaria, dengue fever, and cholera. The U.S. CDC’s FluView app and the ARGONet system are examples of advanced flu-tracking tools (Borda et al., 2022).
Natural Language Generation (NLG)
Natural language generation (NLG) is another AI technology that supports public health efforts. NLG algorithms can create clear and targeted public health messages, ensuring that information is easily understood by the general public (Adams, 2021). This is crucial during health crises when timely and accurate communication can save lives
Conclusion
In the face of increasingly complex global health challenges, AI stands out as a vital tool in our arsenal. From spotting disease outbreaks before they spiral out of control to speeding up drug development and optimizing healthcare delivery, AI is proving its worth in countless ways. While it’s not a silver bullet, the integration of AI into global health strategies offers a path to more effective, efficient, and equitable healthcare worldwide.
However, AI’s use is mostly limited to rich countries, which worsens health inequalities. To fix this, we need international teamwork to improve digital systems in poorer countries. Partnerships between these countries, wealthy nations, and tech companies could help share technology and build skills. It’s also important to create AI solutions that fit each region’s specific needs (Gulumbe et al., 2023).
As we continue to refine and expand AI applications in this field, we move closer to a future where we can respond swiftly and effectively to health crises, saving countless lives in the process.
References
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