Daniel McDuff
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Articles by Daniel McDuff
The anatomy of a personal health agent
Abstract Large language models are enabling a new generation of health agents, yet their ability to address the diverse needs of individuals in everyday health settings remains underexplored1,2. Here we developed a comprehensive personal health agent that reasons over multimodal data from consumer wellness devices and medical records to provide personalized health support.
An open vision-language model for diverse medical applications
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Abstract Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3.
By Sahar Kazemzadeh, Fereshteh Mahvar, Atilla P. Kiraly, Timo Kohlberger, Chufan Gao, Ronnachai Jaroensri, Shawn Xu, Cian O Hughes, Cían Hughes, Charles T. Lau, Justin Chen, Tiffany Chen, Jeremy Lai, Samuel Schmidgall, Lu Yang, Kejia Chen, Kenneth A Philbrick, Mercy Asaa Asiedu, Howard Hu, Howard Yang, Richa Tiwari, Sunny Jansen, Preeti Singh, Yubin Kim, Daniel McDuff, Catherine Kozlowski, Shreya pathak, Nino Vieillard, Sarah Perrin, Tatiana Matejovicova, Thomas Mesnard, Geoffrey Cideron, Michelle Casbon, Jean-Baptiste Alayrac, Vlad Feinberg, Sebastian Borgeaud, Cassidy Hardin, Robert Dadashi, Léonard Hussenot, Armand Joulin, Clement Farabet, Joelle Barral, Tris Warkentin, Jonathon Shlens, David Fleet Verified, Shekoofeh Azizi, Omar Sanseviero, Gus Martins, Phoebe Kirk, Anand Rao, Yossi Matias, Katherine Chou, Avinatan Hassidim, Dale Webster, Shravya Shetty, Yun Liu, David Steiner, Rory Pilgrim, Daniel Golden Verified, Lin Yang, Andrew Sellergren, Madeleine Traverse, Fayaz Jamil, Liron Yatziv, Bram Sterling, Stefanie Anna Baby, Susanna Maria Baby, Per Bjornsson, Shashir Reddy, Ryan Brush, Bashir Sadjad, Ines Mezerreg, Rachelle Sico, Aishwarya Kamath, Johan Ferret, Ramona Merhej, Alexandre Ramé, Morgane Riviere, Louis Rouillard, Jean-bastien Grill, Sabela Ramos, Edouard Yvinec, Elena Buchatskaya, Dmitry Lepikhin, Alek Andreev, Olivier Bachem, Victor Cotruta, Kavi Goel, Can Kirmizibayrak
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Nature
Verified
Capable language models can outgrow the benefits of collaboration
Abstract Agents, language model-based systems that can reason, plan and act with tools to accomplish tasks, are widely deployed, yet it remains unclear when multi-agent coordination outperforms a strong single agent. Here we conduct a controlled experiment that holds task prompts, tools and compute budgets constant while varying only coordination structure and model capability.
Passive heart-rate monitoring during smartphone use in everyday life
Abstract Resting heart rate (RHR) is a key biomarker of cardiovascular health and mortality1,2,3, but passively tracking it longitudinally generally requires a wearable device, limiting its availability. Here we present passive heart-rate monitoring (PHRM), a deep-learning system that uses facial video-based photoplethysmography for passive measurements of heart rate (HR) and RHR during everyday smartphone interactions.
Computer Science > Artificial Intelligence
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Insulin resistance prediction from wearables and routine blood biomarkers
7 hours ago It may be slower and have worse caps, but Claude is still a better fit for me. Recently, I left ChatGPT. There were many reasons for the move, including the company’s transition away from its original non-profit status and its handling of its partnership announcement with the US Department of …
Insulin resistance prediction from wearables and routine blood biomarkers
Abstract Insulin resistance (IR), a primary precursor to type 2 diabetes, is characterized by impaired insulin action in tissues1. However, diagnostic methods remain expensive and inaccessible, which hinders early intervention2,3.
A scalable framework for evaluating health language models - npj Digital Medicine
Abstract Large language models (LLMs) have emerged as powerful tools for analyzing and interpreting complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses when provided with patient-specific health information that encompasses lifestyle, biomarkers, and context.
Transforming wearable data into personal health insights using large language model agents
5 hours ago ChatGPT is a lot simpler to use than you think, and there are many ways to make it work well for you. ChatGPT launched more than three years ago, and the AI chatbot has become an institution. It has around 800 million weekly users -- which means roughly 7 billion people still need to get up to speed. …
Transforming wearable data into personal health insights using large language model agents - Nature Communications
Abstract Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code generation. Large language model (LLM) agents present a promising yet largely untapped solution for this analysis at scale. We introduce the Personal Health Insights Agent (PHIA), a system leveraging multistep reasoning with code generation and information retrieval to analyze and interpret behavioral health data.
Invariant HVC size in female canaries singing under testosterone: Unlocking function through neural differentiation, not growth
Significance In many songbird species, singing can be induced in otherwise nonsinging females or is expressed only during certain life stages. These transient behaviors have been attributed to testosterone-driven growth and regression of song control brain regions. We show that in adult female canaries, the size of these regions remains constant; instead, testosterone alters the phenotype and activity of their component neurons.
Precise gene editing of pathogenic Lamin A mutations corrects cardiac disease
Precise gene editing of pathogenic Lamin A mutations corrects cardiac disease Xurde M. Caravia, Brian Hayashi https://orcid.org/0009-0005-4004-5451, Hui Li https://orcid.org/0000-0002-4114-8349, +7 , Peiheng Gan, Damir Alzhanov, Wei Tan, Kenian Chen, John R. McAnally, Lin Xu, Ning Liu, and Eric N. Olson https://orcid.org/0000-0003-1151-8262 eric.olson@utsouthwestern.edu -7Authors Info & Affiliations Contributed by Eric N.
Smartphone use in a large US adult population: Temporal associations between objective measures of usage and mental well-being
Smartphone use in a large US adult population: Temporal associations between objective measures of usage and mental well-being Edited by Susan Fiske, Princeton University, Jamaica, VT; received January 3, 2025; accepted August 13, 2025 Significance Smartphones are an indispensable tool in everyday life; however, there are concerns about how their use may impact mental health and well-being.
Climate change impacts on tropical cyclone–induced power outage risk: Sociodemographic differences in outage burdens | PNAS
U.S. Department of Energy, Electric emergency incident and disturbance report: Annual summaries. (OE-417) Washington, D.C. (2015). R. J. Campbell, Weather-related power outages and electric system resiliency. Congressional research service report for congress. 7‐5700 R42696 (2012). www.crs.gov. Primen, The cost of power disturbances to industrial and digital economy companies. Primen. TR-1006274 (Available through EPRI). June 29. Madison WI (2001).
A personal health large language model for sleep and fitness coaching - Nature Medicine
Abstract Although large language models (LLMs) show promise for clinical healthcare applications, their utility for personalized health monitoring using wearable device data remains underexplored. Here we introduce the Personal Health Large Language Model (PH-LLM), designed for applications in sleep and fitness. PH-LLM is a version of the Gemini LLM that was finetuned for text understanding and reasoning when applied to aggregated daily-resolution numerical sensor data.
Evidence of Differences in Diurnal Electrodermal, Temperature, and Heart Rate Patterns by Mental Health Status in Free-Living Data
Daniel McDuff, Isaac Galatzer-Levy, Seamus Thomson, Andrew Barakat, Conor Heneghan, Samy Abdel-Ghaffar, Jake Sunshine, Ming Zher-Poh, Lindsey Sunden, John Hernandez, Allen Jiang, Xin Liu, Benjamin W. Nelson were employed by Alphabet and received stock as part of the normal compensation. This study was funded by Google. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
An overview of diagnostics and therapeutics using large language models
REFERENCES , & (2023). Large language models show human-like content biases in transmission chain experiments. Proceedings of the National Academy of Sciences, 120(44), Article e2313790120. https://doi.org/10.1073/pnas.2313790120 , , , , , , , , , , , , , , , , , , , … (2022). Do as I can, not as I say: Grounding language in robotic affordances. arXiv. https://doi.org/10.48550/arXiv.2204.01691 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders ( 5th ed.).
What Does Large-scale Electrodermal Sensing Reveal?
Abstract Electrodermal activity (EDA) is a physiological measure that is used to index sympathetic arousal in response to stressors and other perturbations. However, EDA is underutilized in real-world, population-level research and clinical practice because of a paucity of remote measurement capabilities on commodity devices.
[2308.01834] The Capability of Large Language Models to Measure Psychiatric Functioning
The current work investigates the capability of Large language models (LLMs) that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2) to predict psychiatric functioning from patient interviews and clinical descriptions without being trained to do so.
Seeing Beneath the Skin with Computational Photography
By Ewa Nowara, Daniel Mcduff, Ashutosh Sabharwal, Ashok Veeraraghavan Communications of the ACM, December 2022, Vol. 65 No. 12, Pages 90-100 10.1145/3536421 Comments View as: Print Mobile App ACM Digital Library In the Digital Edition Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook From X-rays to magnetic resonance imaging (MRI), methods for scanning the body have transformed how we understand and care for our health.
CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning in machine learning
The ability to reason about causality, and ask “what would happen if…?’’ is one property that sets human intelligence apart from artificial intelligence. Modern AI algorithms perform well on clearly defined pattern recognition tasks but fall short generalizing in the ways that human intelligence can. This often leads to unsatisfactory results on tasks that require extrapolation from training examples, e.g., recognizing events or objects in contexts that are different from the training set.
Building an AI That Feels
Illustration: Eddie Guy In the past year, have you found yourself under stress? Have you ever wished for help coping? Imagine if, throughout the pandemic, you’d had a virtual therapist powered by an artificial intelligence (AI) system, an entity that empathized with you and gradually got to know your moods and behaviors. Therapy is just one area where we think an AI system that can recognize and interpret emotions could offer great benefits to people.
Building an AI That Feels
In the past year, have you found yourself under stress? Have you ever wished for help coping? Imagine if, throughout the pandemic, you'd had a virtual therapist powered by an artificial intelligence (AI) system, an entity that empathized with you and gradually got to know your moods and behaviors. Therapy is just one area where we think an AI system that can recognize and interpret emotions could offer great benefits to people.
Strategist's Digest: What makes people share ads?
By Daniel McDuff, Microsoft, Jonah Berger, The Wharton School of the University of Pennsylvania Give it to me in one sentence. Ads that make people smile or wrinkle their nose (often in disgust) are more likely to be shared. Give me a little more detail. As the title gives away, the researchers were interested in which emotions were most likely to prompt people to share an ad.
Systematic analysis of video-based pulse measurement from compressed videos
Camera-based physiological measurement enables vital signs to be captured unobtrusively without contact with the body. Remote, or imaging, photoplethysmography involves recovering peripheral blood flow from subtle variations in video pixel intensities. While the pulse signal might be easy to obtain from high quality uncompressed videos, the signal-to-noise ratio drops dramatically with video bitrate.
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards
As people learn to navigate the world, autonomic nervous system (e.g., ``fight or flight) responses provide intrinsic feedback about the potential consequence of action choices (e.g., becoming nervous when close to a cliff edge or driving fast around a bend.) Physiological changes are correlated with these biological preparations to protect one-self from danger.
How would you feel about machines that could feel how you feel?
April 30, 2019 | By Daniel McDuff, Researcher; Ashish Kapoor, Principal Researcher, Research Manager Recent successes in machine intelligence hinge on core computation ability to efficiently search through billions of possibilities in order to make decisions. Sequences of decisions, if successful, often suggest that perhaps computation is catching up to–or even surpassing–human intelligence.
Designing Emotionally Sentient Agents
By Daniel McDuff, Mary Czerwinski Communications of the ACM, December 2018, Vol. 61 No. 12, Pages 74-83 10.1145/3186591 Comments Credit: MDI Digital Today, people increasingly rely on computer agents in their lives, from searching for information, to chatting with a bot, to performing everyday tasks. These agent-based systems are our first forays into a world in which machines will assist, teach, counsel, care for, and entertain us.
Designing Emotionally Sentient Agents
By Daniel McDuff, Mary Czerwinski Communications of the ACM, December 2018, Vol. 61 No. 12, Pages 74-83 10.1145/3186591 Comments Credit: MDI Digital Today, people increasingly rely on computer agents in their lives, from searching for information, to chatting with a bot, to performing everyday tasks. These agent-based systems are our first forays into a world in which machines will assist, teach, counsel, care for, and entertain us.
Do Emotions in Advertising Drive Sales?: Use of facial coding to understand the relation between emotional ads and sales effectiveness
This paper examines the impact of ads that evoke emotions and are entertaining or are memorable on product sales. Consumer sentiment Emotion TV & Connected TV effectiveness France Germany United Kingdom United States Consumer decision making Biometric research Strategy Daniel McDuffMassachusetts Institute of TechnologyRana El KalioubyAffectivaEvan KodraNortheastern UniversityLaurent Larguinet... WARC subscribers can sign in to keep reading Not a subscriber?
Why do some advertisements get shared more than others? Quantifying facial expressions to gain new insights
Why do some advertisements get shared more than others? Using scalable automated facial coding algorithms, the authors quantified the facial expressions of thousands of individuals in response to hundreds of video advertisements.
How Emotion-Sensing Technology Can Reshape the Workplace
New emotion-sensing technologies can help employees make better decisions, improve concentration, and adopt healthier and more productive work styles. But companies must address important privacy issues. advertisement As companies search for new ways to improve performance, some executives have begun paying attention to developments in emotion-sensing technologies (ESTs) and software fueled by artificial emotional intelligence.
A large-scale analysis of sex differences in facial expressions
There exists a stereotype that women are more expressive than men; however, research has almost exclusively focused on a single facial behavior, smiling. A large-scale study examines whether women are consistently more expressive than men or whether the effects are dependent on the emotion expressed. Studies of gender differences in expressivity have been somewhat restricted to data collected in lab settings or which required labor-intensive manual coding.
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