TCCI’s 2025 Spring Newsletter!



	中文

Dear Friends,

It’s that time of year again and we are excited to share with you the Chen Institute’s 2024 Annual Report. Looking back, it was a year of growth, learning and expansion. A new prize, two co-hosted meetings in Shanghai, more Chen Institute training programs, growth of our Chen Scholars Program and the inaugural Chen Scholars Retreat.

We kicked 2025 off with a two-day meeting exploring the current state and future potential of Focused Ultrasound Neuromodulation with leaders in the field from all over the world. More recently, we launched the Tianqiao and Chrissy Chen Ideation and Prototyping Lab at Stanford University, a facility which will bolster medical technology innovation multidisciplinary collaboration on Stanford’s campus.

We look forward to journeying into another year, knowing that we have your ongoing friendship and support.

With kind regards,

Tianqiao Chen Chrissy Luo

TCCI in the News

Stanford Christens the “Tianqiao and Chrissy Chen Ideation and Prototyping Lab”

In a ribbon-cutting ceremony on March 3rd, Chen Institute co-founder, Chrissy Luo, and Stanford University officials inaugurated the Tianqiao and Chrissy Chen Ideation and Prototyping Lab within the Stanford Nanofabrication Facility. This new lab aims to bolster medtech innovations on Stanford’s campus by fostering a community centered around medical device prototyping and multidisciplinary collaboration.

Chen Institute Hosts Global Experts for Two-Day Meeting on FUS Neuromodulation

On January 9-10, the Chen Institute hosted leaders in the field of Focused Ultrasound (FUS) for an invitation-only meeting centered on the current state and future potential of FUS neuromodulation. During the meeting, attendees discussed some of the latest advances and explored the collective, potential impact of FUS neuromodulation for treatment of the brain or mind.

Recognition

Chen Scholar Awarded a Biden Administration Presidential Early Career Award for Scientists and Engineers

Mass General Hospital Chen Scholar Marc Wein, MD, PhD, was awarded a Presidential Early Career Award for Scientists and Engineers (PECASE), an honor bestowed by the U.S. government on outstanding scientists and engineers early in their careers.

Supporting The Community

39th Annual AAAI Conference on Artificial Intelligence

We were pleased not only to support AAAI’s recent annual meeting as a sponsor but also to welcome many of the AI industry’s leading luminaries to a Chen Institute dinner. Francesca Rossi, IBM Fellow and AI Ethics Global Leader at the T.J. Watson Research Center, Eric Horvitz, Chief Scientific Officer at Microsoft and Toby Walsh from University of New South Wales, were just a few of the folks who joined us. They, and others, can be seen in the group photo below.

RESEARCH

Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports

USCF Chen Scholar, Rima Arnout, MD, was part of a team whose research was recently published in the journal Nature focusing on recent innovations in cardiac ultrasound.

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both statistical- and large language model-based techniques.

Artificial intelligence models using F-wave responses predict ALS

Chen Scholar, Nathan P. Staff, M.D., Ph.D. a Professor of Neurology at the Mayo Clinic College of Medicine and Science, and his colleagues recently published research in the journal Brain which focuses on improving the diagnosis and prognosis of amyotrophic lateral sclerosis (ALS), a severe motor neuron disease, using advanced artificial intelligence (AI) techniques. ALS is challenging to diagnose early because its symptoms can mimic other conditions like inclusion body myositis or radiculopathy. The study analyzed nerve conduction F-wave responses from over 46,000 patients, using AI to identify patterns that might indicate ALS. This could lead to earlier interventions, better management of the disease, and improved quality of life for patients. Integrating such AI tools into clinical practice could revolutionize how ALS and similar conditions are diagnosed and treated.