Paul Schmiedmayer
@PSchmiedmayer
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Lead Artificial Intelligence & Assistant Director Digital Health @StanfordBDHG; @Stanford; Working on @StanfordSpezi; @SwiftLang enthusiast (he/him)
Stanford, CA
Joined March 2016
Do you want to work on our @StanfordSpezi ecosystem and exciting other research and translational projects at @Stanford?. Let us know about you by sharing your interest at 🚀.
bdh.stanford.edu
Are you excited about open-source development & digital health? We have several opportunities to work with us and contribute to our efforts! 🚀. We are looking for experts in Swift, Kotlin, and TypeScript: .
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Applications for our Stanford Biodesign Innovation Fellowship are now open! Apply at 🚀.
biodesign.stanford.edu
Every year, we are inspired by the aspiring innovators who step into the Stanford Biodesign Innovation Fellowship—and emerge ready to transform healthcare. Applications are now open for the 2026–27 cohort! .Know someone ready to lead in healthtech innovation?
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RT @StanfordSpezi: 🔵 Easily onboard users to your digital health app with SpeziOnboarding! 🔵. Onboarding in digital health apps can be comp….
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RT @StanfordBDHG: 🤔 How do we navigate the tradeoffs between privacy, cost, and performance when using #LLMs with health data?. Cloud-based….
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We had a great time giving a keynote presentation and in-depth workshop on the @SUBiodesign process at the @HPI_DE Digital Health Innovation Forum last week. A huge effort by all of our @StanfordBDHG team, enabling us to share our research, teaching, and translation efforts 🚀.
We are strengthening connections across the Atlantic!. Our group was invited to give a keynote and host a workshop at the Digital Health Innovation Forum #DHIF2025 in Potsdam, Germany last week and it was a full house! We showcased our team’s research, translation, and
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Incorporating clinicians and computer scientists in a multidisciplinary team is the secret of success; congratulations to @DrAalami, @vishnuravi, @AZahedivash, and everyone in our team who aims to be a medical doctor in the future 🚀.
On National Doctor's Day, we proudly honor our team's exceptional physician leaders — @DrAalami, @vishnuravi, and @AZahedivash — who balance their clinical careers with pioneering work in digital health, as well as the many dedicated physicians we are fortunate to collaborate
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We had a wonderful time introducing students to our CS342 course at Chalmers University of Technology in Sweden! 🇸🇪. Always great to expand the ecosystem and share @StanfordSpezi with more students 🚀.
This past week, students in the Digital Health Implementation track at Chalmers University of Technology in Sweden got an in-person crash course from our teaching team 🚀 . - @DrAalami unveiled the unique challenges and considerations specific to developing digital solutions for
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RT @vishnuravi: Our trip to Sweden to replicate our @StanfordBDHG Building for Digital Health course at Chalmers University of Technology i….
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RT @DrAalami: Sahlgrenska Hospital 🏥 and Chalmers University 🎓have the perfect ecosystem for digital health innovation especially given th….
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Great work by our team; excited about an other great system built using @StanfordSpezi. Great job by @andreas_bauer_ who built NeuroNest!🚀.
Our team at Stanford Digital Health, in collaboration with William Bosl, developed the @StanfordSpezi -based NeuroNest system—a scalable, open-source EEG platform for early neurodevelopmental screening. Learn more at
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Checkout SpeziLLM; a great way to integrate LLMs in digital health applications including on-device LLM execution: 🚀.
github.com
Large Language Model (LLM) module for the Spezi Ecosystem - StanfordSpezi/SpeziLLM
Bringing AI to Digital Health with SpeziLLM!.Built by the Stanford Spezi team, SpeziLLM makes it easy to integrate LLMs into Swift-based health apps. Open-source. Powerful. Ready to use. 👉 @PSchmiedmayer @PhilippZagar @Leon Nissen @Vishnu Ravi
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RT @StanfordBDHG: We were honored to help kick off a new Digital Health Implementation course in collaboration with Chalmers University of….
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Amazing to expand CS342 to Sweden and share the work of @StanfordBDHG and our @StanfordSpezi ecosystem with more students, researchers, and innovators in Sweden! 🇸🇪.
We were honored to help kick off a new Digital Health Implementation course in collaboration with Chalmers University of Technology and Sahlgrenska University Hospital in Gothenburg, Sweden. This course is based on our flagship CS342 Building for Digital Health course at
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RT @StanfordBDHG: We have wrapped up another incredible quarter of the Building for Digital Health course at Stanford with the amazing help….
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RT @StanfordSpezi: Say goodbye to complex Bluetooth integrations! SpeziBluetooth makes it effortless to retrieve and connect to known devic….
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RT @StanfordBDHG: From Classroom to Publication! 🎉📄. Exciting news—our research, which began as a collaboration in Stanford’s Building for….
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Great work by our team and a project that emerged right out of our CS342 class at Stanford 🚀. Excited to see the usage of @StanfordSpezi in more and more research apps!.
From Classroom to Publication! 🎉📄. Exciting news—our research, which began as a collaboration in Stanford’s Building for Digital Health course, has now been published in JMIR Formative Research! 🚀.Enhancing Distress Tolerance Skills in Adolescents With Anorexia Nervosa Through
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RT @StanfordSpezi: Struggling to visualize @Apple #HealthKit data in your SwiftUI iOS app? The new *HealthKitQuery* and *HealthChart* featu….
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RT @vishnuravi: Our latest analysis demonstrates that smartphones can effectively run LLMs on device for medical AI applications requiring….
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Excited to share our latest preprint from our group at @StanfordBDHG ! 🚀.We show that privacy-preserving, on-device AI for medical applications is feasible with real-world benchmarks. Read more & start building with @StanfordSpezi!. 🔗
arxiv.org
The deployment of Large Language Models (LLM) on mobile devices offers significant potential for medical applications, enhancing privacy, security, and cost-efficiency by eliminating reliance on...
Check out our latest preprint examining how LLMs perform at clinical reasoning when run directly on personal smartphones and tablets. Our benchmarks reveal that even standard consumer devices can run LLMs for medical AI applications effectively, demonstrating the potential for
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