Justin Chan
Assistant Professor | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
Justin Chan's research focuses on building intelligent systems for computational health and large-scale environmental sensing.
Biography
Justin Chan is an Assistant Professor in the Electrical and Computer Engineering Department and the Software and Societal Systems Department at Carnegie Mellon University. His research focuses on building intelligent mobile and embedded systems for computational health and large-scale environmental sensing.
His work on smartphone-based ear infections is now FDA-listed and is available to select early access healthcare systems. His work on new-born hearing screening has led to an international effort called TUNE with the goal of bringing universal newborn hearing screening across Kenya as well as collaborations with NGOs such as the Global Foundation for Children with Hearing Loss to deploy this technology in Nepal and Mongolia. His work on contactless cardiac arrest detection has been licensed to a startup which has recently been acquired by Google. He was also a lead contributor for CovidSafe (now WA Notify), a COVID-19 contact tracing and symptom tracking app, which became part of official efforts by the WA Department of Health to manage the pandemic. He has authored publications in interdisciplinary journals like Nature Biomedical Engineering, Science Translational Medicine, Nature Communications as well as Computer Science and Engineering venues like MobiSys, MobiCom, SIGCOMM, SIGGRAPH Asia and UIST.
Areas of Expertise (5)
Enviromental Sensing
Mobile Health
Cardiac Arrest Detection
Contact Tracing
Smartphone-based Ear Infections
Media Appearances (4)
A New Set of Eyes
Carnegie Mellon University College of Engineering News online
2024-10-28
"The goal of our system is to catch these drug administration errors in real-time, before the injection, and provide an alert so the clinician has a chance to intervene before any patient harm," said Justin Chan, an assistant professor in the School of Computer Science's Software and Societal Systems Department and the College of Engineering's Department of Electrical and Computer Engineering.
Geek of the Week: UW’s Justin Chan uses computer science skills to democratize medical devices
GeekWire online
2019-09-13
Chan, our latest Geek of the Week, recently developed a smartphone app that can detect middle ear fluid using sound and a paper cone, and he’s co-founder of Edus Health, a startup aiming to make pediatric healthcare more accessible.
Smartphone App Screens Kids for Ear Problems
Scientific American online
2019-05-16
“Based on the size of the problem, we really wanted to design a technology that could detect it accurately, and also be accessible to a wide audience,” says Justin Chan, a doctoral student at the Paul G. Allen School for Computer Science & Engineering at the University of Washington. Chan is lead author on a new study that tested the app, published this week in Science Translational Medicine.
A Smartphone App And A Paper Funnel Could Help Parents Diagnose Kids' Ear Infections
NPR online
2019-05-15
"All you really need to do to detect ear fluid is use sound," says Justin Chan, a graduate student at the Paul G. Allen School of Computer Science & Engineering at the University of Washington in Seattle.
Media
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Industry Expertise (3)
Biotechnology
Research
Education/Learning
Accomplishments (3)
Scholar (professional)
NIH mHealth Training Institute
Emerging Rockstar (professional)
IEEE Pervasive Computing
Runner-Up, SIGMOBILE Doctoral Dissertation Award (professional)
University of Washington
Education (3)
University of Washington: Ph.D., Computer Science & Engineering 2023
University of Washington: M.S., Computer Science & Engineering 2018
Dartmouth College: A.B., Computer Science 2015
Links (3)
Articles (5)
An Open-Source Smartphone Otoacoustic Emissions Test for Infants
Pediatrics2025 OBJECTIVE Universal hearing screening is essential for early identification of infants with hearing loss, yet there is a lack of low-cost, scalable equipment suitable for resource-constrained settings. Here we test a low-cost smartphone device for infant hearing screening. METHODS Infants aged 0 to 6 months were recruited from 3 ambulatory clinics at Seattle Children’s Hospital with a high prevalence of hearing loss. We compared results from a low-cost open-source distortion product otoacoustic emission (OAE) probe and smartphone app with results from a commercially available OAE device. Hearing status was confirmed using newborn hearing screening, diagnostic testing, or both. Primary outcomes were referral rate as well as sensitivity, specificity, positive predictive value, and negative predictive value compared with known hearing status.
Detecting clinical medication errors with AI enabled wearable cameras
npj Digital Medicine2024 Drug-related errors are a leading cause of preventable patient harm in the clinical setting. We present the first wearable camera system to automatically detect potential errors, prior to medication delivery. We demonstrate that using deep learning algorithms, our system can detect and classify drug labels on syringes and vials in drug preparation events recorded in real-world operating rooms. We created a first-of-its-kind large-scale video dataset from head-mounted cameras comprising 4K footage across 13 anesthesiology providers, 2 hospitals and 17 operating rooms over 55 days. The system was evaluated on 418 drug draw events in routine patient care and a controlled environment and achieved 99.6% sensitivity and 98.8% specificity at detecting vial swap errors. These results suggest that our wearable camera system has the potential to provide a secondary check when a medication is selected for a patient, and a chance to intervene before a potential medical error.
No Ear Left Behind: Wireless Earbuds for Low-Cost Hearing Screening
GetMobile: Mobile Computing and Communications2024 Hearing loss is particularly harmful for language acquisition and neuro-development if it is left undetected in early childhood. Newborn hearing screening technologies using otoacoustic emissions (OAE) rely on detecting soft sounds generated by a healthy cochlea. High-income countries like the United States frequently implement hearing screening programs for every child at birth. However, such universal hearing screening is significantly less common in low- and middle-income countries, partly due to the conventional wisdom that the test requires sensitive and expensive acoustic hardware that costs thousands of dollars.
Intelligent Earable Systems for Equitable Health
GetMobile: Mobile Computing and Communications2024 Health inequities often show up in the global health setting, where the country a person is born in has a huge impact on their ability to access even basic medical resources, like hearing care [1]. This lack of access is often made challenging due to the prohibitively high prices of medical devices, which cannot be afforded by most of the world. Specifically, ear diagnostic hardware, and more broadly audiology as a field, has not seen significant advances in making medical devices more affordable or accessible. As a result, this has led to the continued use of outdated diagnostic equipment that still costs thousands of dollars. At the same time, mobile computing technologies have advanced substantially over the last two decades since the introduction of the first iPhone in 2007.
Bringing Underwater Networking to the 21st Century
GetMobile: Mobile Computing and Communications2023 The state of underwater networking today is similar to ARPANET back in the 1970s, when only a select few people with expensive hardware resources had access to the technology. We present the first acoustic system that brings underwater networking capabilities to existing mobile devices like smartphones and smart watches. Our software-only solution leverages audio sensors, i.e., microphones and speakers, ubiquitous in today's devices, to enable acoustic underwater communication between mobile devices. To achieve this, we design a communication system that adapts in real-time to differences in frequency responses across mobile devices, changes in multipath and noise levels at different locations and dynamic channel changes due to mobility. We evaluate our system in six different real-world underwater environments in the presence of boats, ships and people fishing and kayaking.
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