Saving wildlife with AI, and randomized trials go remote
Aug 22, 2024
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Christine Peterson, a freelance science journalist specializing in wildlife and environmental issues, discusses the hidden impact of global fencing on animal migrations and how AI can help map these barriers to enhance wildlife connectivity. Azizi Seixas, interim chair at the University of Miami, explores the benefits of decentralized randomized trials, arguing they can increase accessibility and equity in medical research. Together, they illustrate how technology can redefine both wildlife conservation and health studies, opening new avenues for engagement and innovation.
Artificial intelligence and aerial photography are being utilized to map wildlife barriers like fences, helping to preserve migration paths.
Decentralized randomized trials enhance medical research by allowing home-based participation, improving data inclusivity and expediting results.
Deep dives
Impact of Fences on Animal Migration
Fences pose significant barriers to wildlife, impacting their migration patterns and survival. In Wyoming, a notable survey revealed that there are over 7,000 kilometers of fences, contributing to high mortality rates among species like pronghorn during harsh winters. Research indicated that collared pronghorn wandered and died after struggling to cross these fences, showing how these structures hinder access to critical resources. Conservationists are now leveraging AI and aerial photography to locate and assess these fences, aiming to minimize their detrimental effects on native wildlife.
Decentralizing Clinical Trials
Decentralized randomized controlled trials (DCTs) offer an innovative approach to medical research by allowing participants to engage from home, thus eliminating barriers associated with travel. Traditional trials often limit participation to those near research centers, but DCTs enable broader access, potentially leading to more representative and robust data. By utilizing digital tools such as wearables and telehealth, researchers can gather real-world data that reflects participants’ actual conditions. This shift not only accelerates research but also enhances inclusivity, addressing disparities in healthcare availability.
Utilizing AI and Digital Solutions in Research
Artificial intelligence is being integrated into conservation and medical research to improve efficiency and data collection. In wildlife studies, AI's capability to analyze aerial images enhances the identification and cataloging of fences, informing decisions regarding their removal or modification. In healthcare, digital biomarkers are being explored to predict the onset of diseases, improving preventive measures through personalized approaches. This blend of AI and digital solutions positions both fields to effectively tackle challenges while fostering better conservation strategies and medical outcomes.
First up this week on the show, uncounted kilometers of fences are strung across the globe. Researchers know they interfere with wildlife migrations and sometimes make finding food and safety difficult for animals. But they don’t know where all these fences are. Freelancer science journalist Christine Peterson joins host Sarah Crespi to discuss how artificial intelligence and aerial photos could help create fence inventories and eventually reopen spaces for native species.
Next, Azizi Seixas, interim chair of the University of Miami Miller School of Medicine’s department of informatics and health data science and a professor in the department of psychiatry and behavioral sciences, discusses his review on decentralized randomized trials. Randomized, controlled trials based in a research center or centers have long been the gold standard for determining the effectiveness of a medical intervention. This week on the podcast, Seixas argues that distributed research designs with home-based measurements and reporting have the potential to speed up research, allow greater participation, and make the results of studies more equitable.
This week’s episode was produced with help from Podigy.