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In a significant stride towards public health, Artificial Intelligence (AI) is being harnessed to combat the opioid crisis in various ways.
Google's Maps tool has expanded its horizons, offering eco-friendly route suggestions in nearly 40 European countries. The suggestions are based on vehicle type and energy efficiency data, aiming to reduce carbon emissions. However, another crucial application of AI is in the prevention of opioid overdoses.
Machine learning models are being used to analyze cognitive, behavioural, and physiological data to detect opioid misuse. A study collected over 9,000 data points from on-body sensors and cognitive tasks in patients prescribed opioids. Using a temporal fusion transformer model, these models can classify misuse status with good sensitivity and specificity, and an area under the curve (AUC) of 0.81. Behavioural responses were found to be stronger predictors than physiological signals, suggesting that AI-powered cognitive assessments combined with sensor data can effectively flag risky opioid use patterns before overdoses occur.
AI screening tools have proven to be as effective as healthcare providers in detecting opioid-related disorders. These tools aid in care transitions and improve linkage and retention in treatment for opioid use disorder (OUD). They can be integrated into clinical workflows to support screening without adding provider burden and to enhance follow-up care.
Beyond opioid misuse detection, AI is also being applied in emergency and injury surveillance contexts to improve the accuracy and timeliness of overdose identification in healthcare settings, supporting faster interventions. AI models have shown strong performance in classifying injury mechanisms and triaging emergency department patients, contributing to overdose surveillance and management.
AI is also leveraged in public health initiatives through custom opioid-related training programs and performance management to enhance prevention efforts and provider education.
In San Francisco, an urban heat mapping project has been launched using volunteer-collected data from sensors installed in cars to identify heat islands in the city. In a different realm, JCPenney has launched an AI-powered tool for online customers, offering personalized skincare and makeup product recommendations based on customer pictures and self-reported data, as well as an augmented reality feature for makeup trials.
Lastly, officials in New York City, along with researchers from City University of New York and New York University, have launched a real-time flood level dashboard using citywide sensor data.
These applications combined offer a promising, data-driven approach to reducing opioid-related risks and overdoses. The future of AI in healthcare and public safety looks bright, with potential to revolutionize our response to crises and improve the quality of life for many.
- The advancements in data-and-cloud-computing technologies enable the storage and analysis of vast amounts of data in public health research, such as the study of opioid misuse, which helps in identifying risky use patterns before overdoses occur.
- Artificial Intelligence (AI) is not only being utilized in opioid crisis combat but also extended to other domains, like transportation, where Google Maps offers eco-friendly routes based on vehicle type and energy efficiency data, aiming to reduce carbon emissions.
- Beyond AI's role in opioid misuse detection and emergency or injury surveillance, it is also being used in various public health initiatives, such as urban heat mapping projects, customized training programs, and performance management, to enhance prevention efforts and improve the overall quality of life.
