Google Research

A prospective multicenter diagnostic study tested whether an AI system could read standardized "blind sweep" ultrasound images and estimate gestational age in real clinics, on mobile devices, with novice operators and unfamiliar hardware. The model was minimally tuned on a small pilot set of 120 participants in Chicago, with Nairobi site data held out as a strictly independent external test; in a primary cohort of 385 participants between 16 and 36 weeks, AI gestational-age estimates were comparable in accuracy to the current standard of care from expert operators using traditional cart-based ultrasound.

A prospective multicenter diagnostic study tested whether an AI system could read standardized "blind sweep" ultrasound images and estimate gestational age in real clinics, on mobile devices, with novice operators and unfamiliar hardware. The model was minimally tuned on a small pilot set of 120 participants in Chicago, with Nairobi site data held out as a strictly independent external test; in a primary cohort of 385 participants between 16 and 36 weeks, AI gestational-age estimates were comparable in accuracy to the current standard of care from expert operators using traditional cart-based ultrasound.

Google Research, with Beth Israel Deaconess Medical Center and Harvard Medical School, used the photoplethysmography and accelerometer signals of a consumer smartwatch to passively identify a persistent hypertensive state without cuff calibration. Rather than estimating systolic and diastolic pressure at a moment in time, the system averages 15-second signal features over multiple days and combines them with height in a regularized logistic regression that outputs a probability. Against 24-hour ambulatory blood pressure monitoring in 196 participants free of known cardiovascular disease and not taking blood pressure medication, initial screening reached 65.8% sensitivity and 90.0% specificity, versus 55.3% sensitivity for concurrent office screening.

Google Research, with Beth Israel Deaconess Medical Center and Harvard Medical School, used the photoplethysmography and accelerometer signals of a consumer smartwatch to passively identify a persistent hypertensive state without cuff calibration. Rather than estimating systolic and diastolic pressure at a moment in time, the system averages 15-second signal features over multiple days and combines them with height in a regularized logistic regression that outputs a probability. Against 24-hour ambulatory blood pressure monitoring in 196 participants free of known cardiovascular disease and not taking blood pressure medication, initial screening reached 65.8% sensitivity and 90.0% specificity, versus 55.3% sensitivity for concurrent office screening.

With multimillion-dollar, three-year funding from Google.org and technical support from Google Research, Ai2 is launching a climate modeling initiative to develop NeuralGCM+, an open-source model. It keeps NeuralGCM's hybrid design, using physical equations for large-scale dynamics and AI for small-scale processes such as cloud formation and rainfall, and couples the atmosphere with the ocean and sea ice to target stable simulations spanning decades. Google Research's earlier NeuralGCM simulated a year of global atmospheric conditions in eight minutes on a single TPU in its atmosphere-only version.

With multimillion-dollar, three-year funding from Google.org and technical support from Google Research, Ai2 is launching a climate modeling initiative to develop NeuralGCM+, an open-source model. It keeps NeuralGCM's hybrid design, using physical equations for large-scale dynamics and AI for small-scale processes such as cloud formation and rainfall, and couples the atmosphere with the ocean and sea ice to target stable simulations spanning decades. Google Research's earlier NeuralGCM simulated a year of global atmospheric conditions in eight minutes on a single TPU in its atmosphere-only version.

Boundaries of subjective categories such as "healthy food" shift with culture and person, yet conventional workflows demand stable labels from the start. Google Research's Agile Deliberation splits classifier building into concept scoping and concept iteration: users break a broad concept into sub-categories and explicit exclusions, then a vision-language model surfaces semantically borderline images for the user to judge, with feedback routed into an automatic prompt-optimization loop. Across 18 live 1.5-hour sessions, classifiers built this way substantially outperformed the compared standard baselines on the most challenging subjective images, and participants reported less fatigue and anxiety.

Boundaries of subjective categories such as "healthy food" shift with culture and person, yet conventional workflows demand stable labels from the start. Google Research's Agile Deliberation splits classifier building into concept scoping and concept iteration: users break a broad concept into sub-categories and explicit exclusions, then a vision-language model surfaces semantically borderline images for the user to judge, with feedback routed into an automatic prompt-optimization loop. Across 18 live 1.5-hour sessions, classifiers built this way substantially outperformed the compared standard baselines on the most challenging subjective images, and participants reported less fatigue and anxiety.

Tuberculosis still kills an estimated 1.23 million people a year, and high-burden regions lack the radiologists who read chest X-rays. Google Research and Google for Health handed its chest X-ray algorithms to Pretoria-based Nexus AI, which turned them into a CE-certified clinical diagnostic tool now deployed in about 40 healthcare facilities across six countries including South Africa, Sierra Leone and Côte d'Ivoire. More than 90 clinicians have evaluated over 25,000 chest radiographs, cutting turnaround from weeks to under 45 seconds for rapid triage, with final clinical decisions left to health workers.

Tuberculosis still kills an estimated 1.23 million people a year, and high-burden regions lack the radiologists who read chest X-rays. Google Research and Google for Health handed its chest X-ray algorithms to Pretoria-based Nexus AI, which turned them into a CE-certified clinical diagnostic tool now deployed in about 40 healthcare facilities across six countries including South Africa, Sierra Leone and Côte d'Ivoire. More than 90 clinicians have evaluated over 25,000 chest radiographs, cutting turnaround from weeks to under 45 seconds for rapid triage, with final clinical decisions left to health workers.

Google Research has connected AI agents to wet-lab liquid-handling robots so that design, experiment and sequencing readout form a closed loop. Previously agents could only do computational design while real experimental readouts lagged. Using DNA aptamers as a testbed, agents revised their own rules after two failed rounds, raising confirmed on-target hit rates to 42.1% for α-thrombin and 33.5% for myoglobin.

Google Research has connected AI agents to wet-lab liquid-handling robots so that design, experiment and sequencing readout form a closed loop. Previously agents could only do computational design while real experimental readouts lagged. Using DNA aptamers as a testbed, agents revised their own rules after two failed rounds, raising confirmed on-target hit rates to 42.1% for α-thrombin and 33.5% for myoglobin.

On 57 day-ahead test days for a university teaching building, pretrained TimesFM 3.0 with covariates reached 19.97 kWh hourly mean absolute error, about 43% below the weekly seasonal-naive baseline, without training building-specific weights.

On 57 day-ahead test days for a university teaching building, pretrained TimesFM 3.0 with covariates reached 19.97 kWh hourly mean absolute error, about 43% below the weekly seasonal-naive baseline, without training building-specific weights.

Google Research released a workshop report on agentic privacy and security that argues for grounding agent behavior in Contextual Integrity, turning high-level social norms into machine-readable policy that judges whether an information flow is appropriate before an action runs.

Google Research released a workshop report on agentic privacy and security that argues for grounding agent behavior in Contextual Integrity, turning high-level social norms into machine-readable policy that judges whether an information flow is appropriate before an action runs.