The conference elaborated on exciting ways that AI can be applied to limit the spread of COVID-19. It included a discussion about Taiwan, where data analytics played a significant role in providing public health alerts, identifying risk hotspots, and containing the progression of COVID-19. Dr. Jason Wang discussed how Taiwan used online immigration declaration cards to identify travelers from high-risk areas like Wuhan, apply immigration controls, and initiate quarantines more efficiently. Taiwan then leveraged big data by merging this immigration information with national health insurance data to identify these high-risk travelers when they sought medical care so they could then be triaged and treated in isolation. Taiwan also used big data to identify surgical mask quantities and quickly ramp up production to ensure a sufficient supply. The Taiwanese government then used this information to create a public-facing app so individuals could find personal protective equipment in stock at local drugstores.
The conference also discussed the potential application of AI in accelerating vaccine development to prevent the transmission of COVID-19 and drug testing to treat patients with the virus. According to Dr. Stefano Rensi, the average new drug takes five to ten years of research and clinical trials to reach the market, but an effective treatment for COVID-19 is needed more quickly. Dr. Rensi evaluated the use of AI, particularly the data mining of existing research and the modeling of protein structures, to identify existing drugs that already have been tested for safety and can be progressed to the clinical trial stage expeditiously. This use of AI can potentially save lives by accelerating the pace of medical research, drug development, and treatment.
The conference also highlighted that some applications of AI being developed for the COVID-19 pandemic potentially could be used after the current public health crisis has passed. For example, Dr. Fei-Fei Li discussed using AI-powered smart sensor technology installed in the homes of the elderly to manage the progression of mild COVID-19 symptoms, as well as pre-existing chronic health conditions. Camera, depth sensors, and thermal sensors could detect data that, once encrypted and transferred to a secure central server, could be analyzed using AI models designed to recognize clinically-relevant patterns, including respiratory, sleep, and eating patterns. Thermal sensors, in particular, could allow for fever, mobility, and other critical clinical symptom detection without the more intrusive use of cameras. Caretakers and medical professionals then could be alerted to the models’ findings. According to Dr. Li, AI-assisted in-home elder care has the potential to support the well-being of seniors in a pandemic setting and to revolutionize the ability of seniors to continue living at home even with chronic conditions. This could help mitigate the potential spread of contagious disease and provide other benefits.
The use of AI and other digital health technologies has garnered attention from policy makers as well as industry during the COVID-19 crisis, and more information about these efforts can be obtained here. These types of AI and other technology solutions, however, do require careful legal consideration. Here is a checklist of some regulatory and other legal issues to consider.