AI - Google Research | Techlist.io

googleJun 12, 20263 min readCurated summary

Research into how AI can help users understand skin conditions

Google Research examined how AI tools can help non-experts understand skin conditions and decide what to do next. In a large study, AI substantially improved people’s ability to identify possible conditions, but it did not reliably improve the accuracy of recommended next steps. The research therefore argues that dermatology AI should be designed around human decision-making, safety, and clear guidance—not diagnosis alone. ## Why Dermatology AI Needs Human-Centered Research - More than half of adults use the Internet for health information, and about one-third use AI. - People often lack the medical vocabulary needed to search effectively—for example, searching for “red dots on legs” instead of “palpable purpura.” - Google Research has developed dermatology AI models, validated their generalization, and released datasets such as SCIN. - Earlier research found that online tools can improve condition recognition without necessarily helping people choose appropriate next steps. - The researchers emphasize studying how people interpret and act on AI-generated information. ## Large-Scale Evaluation of an AI Information Tool - A JAMA Dermatology study involved 2,345 participants reviewing de-identified skin-condition cases with images and structured medical histories. - Participants were assigned to one of three groups: - **Standard-search control:** Used familiar text-based search tools. - **AI group:** Used a prototype showing 3–7 AI-predicted conditions, textbook images, and information about symptoms and treatments. - **“Wizard of Oz” control:** Used the same interface, but with dermatologist-provided differential diagnoses presented as if generated by AI. - The AI interface increased participants’ willingness to name a condition: - More than 62% attempted a diagnosis with AI. - Only 41% did so using standard search. - Accuracy also improved: - AI users correctly identified a matching condition about 23% of the time. - Standard-search users achieved 8%. - The “perfect-prediction” interface reached 36%, showing that even accurate candidate lists did not make users nearly perfect. - AI users reported greater confidence, satisfaction, and satisfaction with the time spent searching. ## Identifying a Condition Does Not Guarantee Safe Action - The prototype intentionally avoided prescribing actions or making individualized diagnoses. - Treatment information was dermatologist-written and based on the condition name, rather than the severity or details of the specific case. - Choosing the right next step—such as home care, routine care, or urgent evaluation—remained difficult. - Next-step accuracy improved only slightly in the “Wizard of Oz” group, from 60% in the standard-search control to 63.5%. - The standard AI group showed no statistically significant improvement. - AI users were slightly more likely than control participants to recommend a less urgent action than dermatologists would: 30% versus 27%. - These findings show that identifying possible conditions is insufficient without stronger safety-oriented guidance. ## Studying Real Users and Diverse Communities - The researchers also conducted a qualitative study, published at ACM CHI, to examine how people use AI for their own active skin concerns. - The project partnered with Stanford’s Healthcare AI Applied Research Team and the Santa Clara Family Health Plan. - The community included many Medi-Cal users who rely on a healthcare safety net. - Researchers aimed to gather richer feedback than survey-based studies provide by observing real-world use. - Because participants spoke four primary languages, the application was translated into those languages, with multilingual volunteers or staff available to support communication. AI can make dermatology information easier to find and improve recognition of possible conditions, but it should not be treated as a substitute for professional judgment. Future tools should focus equally on urgency assessment, personalized context, uncertainty, and clear recommendations for when to seek medical care.

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AI Machine Learning Healthcare Ai Differential Diagnosis+3

googleMay 19, 20262 min readCurated summary

Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery

Empirical Research Assistance (ERA) is a Google AI system designed to help scientists develop expert-level computational models. Using Gemini, it searches literature, generates and evaluates code, and explores thousands of possible solutions through tree search. A Nature paper reports strong performance across scientific benchmarks, while new applications suggest ERA can accelerate research in health, climate, energy, and economics. ## How ERA Supports Scientific Coding - ERA starts with a scientific problem and a success metric. - It searches relevant research, combines methods, writes code, and iteratively tests and improves solutions. - Its tree-search process evaluates thousands of alternatives to optimize the resulting model. - Benchmarks in genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics showed expert-level performance. ## Applications to Open Scientific Problems - **Epidemiological forecasting** - Predicted U.S. hospital admissions up to four weeks ahead for flu, COVID-19, and RSV. - Forecasts ranked at or near the top of CDC leaderboards. - The techniques can potentially be adapted to other countries and diseases. - **California water-supply forecasting** - Produced seasonal runoff predictions for snow-fed river basins. - Delivered more accurate early forecasts than California’s official Bulletin 120 outlook. - Improved predictions could support water management and agriculture. - **Atmospheric carbon dioxide monitoring** - Combined geostationary weather-satellite data with other inputs to estimate CO₂ concentrations every 10 minutes across broad areas. - Captured urban emissions, plant-driven daytime absorption, and other atmospheric cycles. - Provides higher spatial and temporal coverage than measurements from satellites such as Orbiting Carbon Observatory-2. - **Solar-energy design** - Combined ERA with Google Antigravity to optimize three-dimensional solar-panel geometries. - Identified a 500-triangle volumetric fan design that could capture scattered radiation without backward shading. - **Retail forecasting** - Used economic indicators, Google Trends, historical patterns, and consumer sentiment. - Matched or exceeded commercial consensus forecasts and the Chicago Fed’s monthly retail forecast. ## Computational Discovery - Google is gradually opening access to Computational Discovery through a trusted tester program in Google Labs. - The system combines ERA with AlphaEvolve to support computational scientific investigation. - It complements other Gemini for Science experiments: - **Hypothesis Generation**, built with AI Co-Scientist, supports developing scientific hypotheses. - **Literature Insights** supports research and literature analysis. ERA’s demonstrated value lies in automating the labor-intensive cycle of designing, testing, and refining scientific software. Its expanding applications indicate that AI-assisted computational research could broaden access to advanced modeling while helping experts investigate complex scientific problems more quickly.

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AI Machine Learning Gemini Time Series Forecasting+4

googleApr 29, 20262 min readCurated summary

Four ways Google Research scientists have been using Empirical Research Assistance

Empirical Research Assistance (ERA) is being used by Google researchers to tackle practical scientific problems rather than only benchmark exercises. Early applications span public-health forecasting, cosmology, and climate monitoring, showing that AI can improve prediction, solve difficult mathematical problems, and extract new value from existing data. The results suggest ERA could make advanced computational research more accessible while producing interpretable, scientifically grounded models. ## Public Health Forecasting - Google expanded ERA-based hospitalization forecasts from COVID-19 to influenza and RSV. - The team submits weekly forecasts for every U.S. state, covering horizons of up to four weeks. - Google forecasts have performed at or near the top of public CDC flu and COVID-19 leaderboards, with similarly strong internal results for RSV. - Forecast accuracy is evaluated using the Weighted Interval Score on log-transformed hospitalization data. - This approach could broaden access to epidemiological modeling and support forecasting for more diseases and regions. ## Cosmology: Cosmic Strings - Cosmic strings are theoretical spacetime defects that may emit gravitational radiation. - Calculating their radiation spectrum is difficult because the governing equations contain singularities. - Earlier work found only a partial solution for a square loop with an angle of 90 degrees. - By combining ERA with Gemini Deep Think, researchers derived six general solutions and a concise formula for the asymptotic limit. - The result demonstrates how AI systems can help explore advanced mathematical techniques and address previously unsolved cosmological problems. ## Climate Monitoring with Weather Satellites - Existing CO₂ satellites provide highly precise but infrequent and geographically limited measurements. - Geostationary satellites such as GOES East scan large areas every 10 minutes, but were not designed to measure CO₂. - Researchers used ERA to create a physics-guided neural network that combines: - 16 GOES East wavelength bands - Lower-troposphere meteorology - Solar angles - Time of year - Trained using sparse OCO-2 and OCO-3 observations, the model estimated column-averaged CO₂ continuously across the satellite’s coverage area. - Comparisons with independent satellite and ground-based observations showed that it captured real CO₂ variation. - The work illustrates how AI can repurpose existing instruments and improve the value of expensive scientific datasets. ERA’s early applications indicate that AI-assisted empirical software can support accurate forecasting, novel mathematical discovery, and higher-resolution environmental monitoring. Its greatest potential may lie in combining domain expertise with existing data and infrastructure to solve problems that would otherwise require substantial time and specialized resources.

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AI Neural Networks Geospatial Analysis Data Collection+4

googleApr 16, 20263 min readCurated summary

AI-generated synthetic neurons speed up brain mapping

Google Research developed MoGen, an AI model that generates realistic synthetic neuron geometries to improve brain-mapping systems. Adding these synthetic examples to PATHFINDER reduced neuron reconstruction errors by 4.4%, primarily by preventing incorrect merges. At the scale of a complete mouse brain, the improvement could eliminate an estimated 157 person-years of manual proofreading. ## Connectomics and the Reconstruction Challenge - Connectomics creates brain wiring maps by imaging thin tissue slices, aligning them, and reconstructing 3D neurons. - AI assists with segmentation and reconstruction, but human experts must still correct errors. - Larger brains pose major scaling challenges: the fruit fly map contains about 166,000 neurons, while a mouse brain is roughly 1,000 times larger. ## Why Neuron Shape Matters - Neurons have complex structures, including long axons, branching dendrites, dendritic spines, and synapses. - PATHFINDER identifies neurite segments and combines them into complete neurons. - Poor or ambiguous microscopy data can cause: - **Split errors:** connected neurites are separated. - **Merge errors:** unrelated neurites are incorrectly joined. - Correcting these mistakes manually is one of the most time-consuming parts of brain mapping. ## MoGen’s Synthetic Neurons - MoGen uses point-cloud flow matching to transform random 3D point clouds into realistic neuronal shapes. - It was trained on surface samples from 1,795 human-verified mouse axons. - Experts could not reliably distinguish MoGen-generated neurite fragments from real ones. - The synthetic data reproduced features such as bending, twisting, thickening, and branching. ## Results with PATHFINDER - Training PATHFINDER with 10% MoGen-generated data reduced reconstruction errors by 4.4%. - The largest gains came from reducing merge errors. - Millions of synthetic neuron shapes were added to the training pipeline. - Although the percentage improvement is modest, it could save the equivalent of 157 years of expert proofreading for a full mouse-brain map. ## Future Applications - MoGen could be tuned to generate neuron types with specific lengths, branching patterns, or spatial ranges. - Future versions may focus on geometries that are particularly likely to cause reconstruction errors. - Google has also trained species-specific models for zebra finches and fruit flies. - The team is exploring synthetic electron-microscopy images to improve earlier stages of reconstruction. - MoGen and its species-specific models have been released as open source. Synthetic neuron generation is a practical way to expand training data without requiring additional manual annotation. Combined with targeted generation and synthetic microscopy, it could help make large-scale projects such as complete mouse-brain mapping more feasible.

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AI Synthetic Data Neural Networks Connectomics+4

googleApr 8, 20263 min readCurated summary

Improving the academic workflow: Introducing two AI agents for better figures and peer review

AI is being positioned as an active participant in academic research, not merely a tool for drafting text. The post introduces PaperVizAgent, which creates publication-ready figures, and ScholarPeer, which produces literature-grounded peer reviews. Both use multi-agent workflows and iterative verification to reduce researchers’ administrative burden while improving visual quality and review rigor. ## PaperVizAgent: Generating Publication-Ready Figures - PaperVizAgent converts manuscript text and a detailed figure caption into academic illustrations. - It uses five specialized agents: - **Retriever:** Finds relevant literature and reference figures. - **Planner:** Organizes the technical content. - **Stylist:** Develops appropriate visual and aesthetic guidelines. - **Visualizer:** Produces images or executable Python code for statistical plots. - **Critic:** Checks the result against the source text and requests revisions. - The critic-driven refinement loop is designed to ensure that figures are both technically faithful and visually clear. - Inputs typically include: - The manuscript’s method or technical sections. - A communicative-intent description explaining what the figure should convey. ### Evaluation Results - PaperVizAgent was compared with direct prompting, few-shot prompting, GPT-Image-1.5, Nano-Banana-Pro, and Paper2Any. - Figures were scored from 0 to 100 on: - Faithfulness - Conciseness - Readability - Aesthetics - It achieved an overall score of **60.2**, exceeding the human baseline of **50.0** and outperforming the evaluated automated systems. - Its strongest results were in conciseness and aesthetics, while its statistical plots reached human-competitive quality. ## ScholarPeer: Automating Rigorous Peer Review - ScholarPeer is a search-enabled, context-aware multi-agent system designed to emulate the workflow of a senior academic reviewer. - Rather than treating review as simple text generation, it combines literature retrieval, adversarial checking, and technical verification. - Its main components include: - A **sub-domain historian** that builds a current domain narrative from literature. - A **baseline scout** that searches for overlooked datasets, methods, and comparisons. - A **multi-aspect Q&A engine** that tests novelty and technical claims. - A **review generator** that follows conference-specific review guidelines. - The resulting review includes a summary, strengths, weaknesses, and questions for the authors. ### Evaluation Results - ScholarPeer was evaluated on public datasets against fine-tuned models and other agentic reviewing systems. - Its active web-search and verification process produced highly critical reviews grounded in existing research. - Side-by-side evaluations showed strong win rates against competing automated reviewers. - The system also narrowed the gap between AI-generated reviews and human reviews in terms of realism, diversity, and alignment with expert judgments. ## Implications for Academic Research - The two agents address separate bottlenecks in the publication process: - PaperVizAgent improves technical communication through better figures. - ScholarPeer helps scale peer review amid growing submission volumes and reviewer fatigue. - Their multi-agent designs suggest that specialized agents, coordinated through retrieval and iterative critique, may be more effective than a single general-purpose language model. - The systems are intended to support researchers rather than replace scientific judgment. Researchers could use PaperVizAgent for early figure prototyping and ScholarPeer for preliminary, literature-informed critique, while retaining human oversight for final scientific and editorial decisions.

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AI Large Language Models Multi Agent Systems Data Visualization+4

googleMar 17, 20263 min readCurated summary

Improving breast cancer screening workflows with machine learning

Google Research’s AIMS studies evaluated whether machine learning could support the UK’s mammography double-reading workflow. Across five NHS screening services, the AI system improved cancer detection sensitivity without reducing specificity, detected some cancers missed by human readers, and processed cases far faster. The studies also showed that safe deployment requires local calibration, monitoring for distribution shifts, and evaluation of how clinicians interact with AI results. ## NHS Screening Challenges - The UK NHS uses two human readers for each mammogram, with arbitration when their assessments require review. - A projected shortage of clinical radiologists—currently around 30% and expected to reach 40% by 2028—threatens the sustainability of this model. - AI could help increase detection while reducing pressure on radiology services. ## Study 1: Standalone Performance - The retrospective evaluation included mammograms from approximately 116,000 women screened across five NHS services. - The services represented three different double-reading and arbitration workflows. - AI thresholds were calibrated separately for each service to account for local populations and procedures. - Performance was measured against the original first reader using a 39-month follow-up period, including interval and subsequent-round cancers. - Researchers also assessed: - Comparisons with second and consensus readers - Lesion-level localization - Performance across demographic groups ## Study 1: Results - Cancer detection increased from 7.54 to 9.33 cases per 1,000 women. - The AI system achieved significantly higher sensitivity than the original first reader without compromising specificity. - It detected 25% of interval cancers missed by the original double-reading process. - Performance was especially strong for invasive cancers and women attending their first screening. - The study found no notable systematic disparities by age, ethnicity, breast density, or socioeconomic status. ## Prospective Technical Deployment - The system was deployed non-interventionally at 12 sites across two London screening services. - It processed 9,266 cases over roughly two months per service. - Mammograms were pseudonymized and sent to a secure Google Cloud-based system. - Median AI processing time was 17.7 minutes, compared with more than two days for the first human read. - The deployment detected a distribution shift between historical training data and current clinical data. - Researchers adjusted operating points during deployment to maintain safe and appropriate recall rates for local workflows. ## Study 2: AI in the Double-Reading Workflow - The second study examined how human readers performed when using AI as part of arbitration, rather than evaluating AI in isolation. - Twenty-two readers reviewed thousands of cases using real screening-service rules. - Two workflows were compared: - **Standard care:** decisions from the historical first and second human readers - **AI-enabled care:** the historical first-reader decision paired with the AI decision - This design aimed to assess the practical effects of replacing the second human read with an AI reader. The findings support AI as a potential second reader in breast cancer screening, but broader prospective clinical validation is still needed. Successful adoption should include phased deployment, local calibration, continuous monitoring, and careful evaluation of human-AI decision-making.

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AI Database Design Machine Learning Cloud Computing+4

googleMar 17, 20263 min readCurated summary

Google Research at The Check Up: from healthcare innovation to real-world care settings

Google Research argues that AI is entering a new phase in healthcare: moving beyond isolated tools toward personalized care, clinical collaboration, public-health planning, and scientific discovery. The post highlights research partnerships, open models, and real-world deployments designed to make healthcare more accurate, accessible, and proactive. Google emphasizes that these advances must be developed responsibly through clinical validation, peer review, and collaboration with healthcare institutions. ## AI for Personalized Healthcare - A Fitbit collaboration studied how AI could support preventative care across the United States. - The research found that a Personal Health Agent (PHA) modeled on a collaborative health team could provide more effective long-term support than single-purpose fitness or tracking apps. - The PHA combines: - Data analysis - Medical and domain expertise - Health coaching - Large multimodal models can transform wearable data into personalized guidance about sleep, fitness, and overall health. ## AI as a Clinical Collaborator - Google’s breast cancer research with Imperial College London and the UK’s NHS used diverse datasets and expert-validated ground truth data. - The experimental system identified 25% of “interval cancers”—cancers missed during screening and later detected after symptoms appeared. - Integrated into clinical workflows, the system could reduce radiologists’ workload while maintaining safe detection performance. - Google’s diabetic retinopathy screening model has been deployed through partnerships with medical institutions in India, Thailand, and Australia. - It has supported more than one million screenings. - Patients can receive results in roughly two minutes. - AMIE, a multi-agent medical AI system, can reason across medical histories, laboratory results, and medical images to identify overlooked patterns. - Google is testing AMIE with Beth Israel Deaconess Medical Center to assist with pre-visit history-taking and flag urgent symptoms. - An IRB-approved national study with Included Health will evaluate AI-supported telehealth care. ## Open Models for Healthcare Developers - Google’s Health AI Developer Foundations (HAI-DEF) provides free open-weight models and open-source tools for building healthcare applications. - MedGemma supports: - Medical text and image interpretation - High-dimensional 3D imaging - Medical-specific speech recognition - The All India Institute of Medical Sciences is using MedGemma for outpatient triage and dermatology screening. - Singapore’s Ministry of Health is adapting the model for locally relevant primary- and specialty-care applications. - The MedGemma Impact Challenge received more than 850 submissions aimed at turning AI research into practical, human-centered healthcare tools. ## AI for Public Health - Google Earth AI combines geospatial models and datasets to study connections between environmental conditions, population behavior, and health outcomes. - Researchers at Mount Sinai and Boston Children’s Hospital/Harvard used Google data and surveys to estimate childhood MMR vaccination coverage at ZIP-code resolution. - The resulting “super-resolution” maps identified pockets of under-vaccination that corresponded with recent measles outbreaks. - Such analysis could help public-health officials target outreach and prevention efforts more effectively. ## AI for Biomedical Discovery - Co-Scientist and Gemini Deep Think are being used to generate scientific hypotheses and support research across fields including single-cell analysis, public health, and neuroscience. - Google is also exploring evolutionary coding agents that run scientific-computing experiments in parallel. - DeepSomatic, a genomic analysis tool, is designed to improve the detection of cancer-related genetic mutations across multiple cancer types. Google’s broader recommendation is to treat AI as a validated collaborator and infrastructure layer rather than a replacement for clinicians or researchers. Continued clinical testing, expert oversight, transparent publication, and open developer access will be essential to translating these systems into safe, practical benefits.

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AI Data Science Telehealth Public Health+4

googleMar 12, 20263 min readCurated summary

Protecting cities with AI-driven flash flood forecasting

Google Research is expanding Flood Hub with urban flash flood forecasts that can provide up to 24 hours’ warning. The system addresses the lack of historical flood observations by using Gemini to extract verified events from public news reports, creating the Groundsource dataset for model training. Its global, lower-resolution approach aims to extend useful warnings to regions that lack expensive sensors and forecasting infrastructure, particularly in the Global South. ## The Need for Earlier Flash Flood Warnings - Flash floods cause roughly 85% of flood-related deaths worldwide and kill more than 5,000 people annually. - They often develop within six hours of intense rainfall, making rapid warnings essential. - Even 12 hours of warning can reduce flood damage by about 60%. - Early warning coverage remains highly unequal: fewer than half of developing countries have access to multi-hazard warning systems. - Flood Hub previously focused mainly on slower-moving riverine floods, covering more than 2 billion people across 150 countries. ## The Data Problem: “Invisible” Floods - River flood models can rely on stream gauges that record water levels and flow. - Flash floods may occur far from gauges, especially in cities where rainfall, impermeable surfaces, drainage, and terrain interact unpredictably. - Building detailed physical simulations globally would be computationally expensive. - Historical, precisely located flash flood records are also scarce, preventing conventional supervised machine learning. - Google’s Groundsource method uses Gemini to analyze public news reports, verify flood locations and times, and assemble a historical flash flood dataset. ## Scaling from Local Systems to Global Coverage - Local flash flood systems can be highly accurate using rain sensors, radar, water-level monitors, and flow measurements. - These systems are expensive to deploy and require location-specific calibration and engineering expertise. - Broader systems such as WMO’s FFGS, ERIC, and the U.S. NWS warning system depend on high-resolution maps, radar forecasts, and skilled hydrologists. - Those resources are often unavailable in the Global South. - Google’s model instead uses globally available products, including NASA IMERG, NOAA CPC, ECMWF’s IFS HRES forecasts, and Google DeepMind’s medium-range weather model. - Forecasts currently operate at a 20-by-20-kilometer resolution, constrained by the resolution of global data sources. ## The Urban Flash Flood Model - The model estimates whether a flash flood is likely in a given area during the next 24 hours. - It uses a recurrent neural network with a long short-term memory (LSTM) component to process meteorological time series. - Inputs also include static geographic and human-environment factors: - Urbanization density - Topography - Soil absorption rates - The initial rollout targets urban regions, where news coverage is denser and most of the world’s population lives. - It currently predicts impacts in areas with population densities above 100 people per square kilometer. ## Evaluation and Reported Performance - Precision was measured against the Groundsource dataset, but raw precision likely understates actual performance because some genuine floods are never reported. - A manual review of 100 alerts per continent found that many apparent false positives were confirmed flood events. - Recall was also evaluated against major floods recorded by the Global Disaster Awareness and Coordination System (GDACS). - Results indicate comparable precision and recall in regions such as South America and Southeast Asia and in wealthier countries with better instrumentation. The approach demonstrates how AI and unstructured public information can help provide scalable flash flood warnings where conventional monitoring infrastructure is limited. Its current urban focus and 20-kilometer resolution make it a broad early-warning tool rather than a replacement for highly localized sensor networks.

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AI Machine Learning Data Analytics Flood Forecasting+4

googleJan 14, 2026Original article

Unlocking health insights: Estimating advanced walking metrics with smartwatches (opens in new tab)

Google researchers have validated that smartwatches are a highly reliable and accurate platform for estimating complex spatio-temporal gait metrics, rivaling the performance of smartphone-based methods. By utilizing a multi-head deep learning model, the study demonstrates that wrist-worn devices can provide continuous, lab-grade health insights into a user's walking speed, step length, and balance without requiring the specific pocket placement or specialized laboratory equipment previously necessary for such data. ## Multi-Head Deep Learning for Wrist-Based Sensors * The researchers developed a temporal convolutional network (TCN) architecture designed to process raw inertial measurement unit (IMU) data, specifically 3-axis accelerometer and gyroscope signals sampled at 50 Hz. * Unlike traditional models that only track temporal events and are prone to integration drift, this multi-head approach directly estimates both unilateral and bilateral metrics simultaneously. * The model architecture extracts embeddings from the IMU signals and concatenates them with user height (a demographic scalar input) to improve the precision of spatial predictions. * The system estimates a comprehensive suite of metrics, including gait speed, double support time (the proportion of time both feet are on the ground), step length, swing time, and stance time. ## Large-Scale Validation and Study Protocol * To ensure rigorous results, the study involved a diverse cohort of 246 participants across two international sites, generating approximately 70,000 walking segments. * Ground truth measurements were captured using a professional-grade Zeno Gait Walkway system to provide high-precision reference data for comparison. * The study protocol included various walking conditions to test the model's versatility: a self-paced six-minute walk test (6MWT), fast-paced walking, and induced physical asymmetry created by wearing hinged knee braces at specific angles. * Researchers employed a five-fold cross-validation strategy, ensuring that all data from a single participant remained within a single split to prevent data leakage and ensure the model generalizes to new users. ## Clinical Validity and Comparative Performance * Smartwatch estimates demonstrated strong validity and excellent reliability, with Pearson correlation coefficients (r) and intraclass correlation coefficients (ICC) exceeding 0.80 for most metrics. * Performance comparisons showed non-significant differences in Mean Absolute Percentage Error (MAPE) between the Pixel Watch and Pixel phone, establishing the smartwatch as a viable alternative to smartphone-based tracking. * While double support time showed slightly lower but acceptable reliability (ICC 0.56–0.60), other metrics like step length and gait speed proved highly consistent across different walking speeds and styles. * The model’s success suggests that smartwatches can effectively bridge the gap in gait analysis, providing a more practical and consistent platform for continuous health tracking than handheld devices. This research establishes smartwatches as a powerful tool for longitudinal health monitoring, enabling the detection of neurological or musculoskeletal changes through passive, continuous gait analysis in everyday environments.

AI Deep Learning Wearables Health Tech+5

googleJan 12, 2026Original article

Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR (opens in new tab)

Google Research has introduced MedGemma 1.5 4B and MedASR, expanding its suite of open medical AI models to support more complex clinical workflows. These updates significantly enhance the interpretation of high-dimensional imaging and medical speech-to-text, providing a compute-efficient foundation for healthcare developers to build upon. By maintaining an open-access model available on Hugging Face and Vertex AI, Google aims to accelerate the integration of multimodal AI into real-world medical applications. ### Multimodal Advancements in MedGemma 1.5 The latest update to the MedGemma 4B model focuses on high-dimensional and longitudinal data, moving beyond simple 2D image interpretation. * **3D Medical Imaging:** The model now supports volumetric representations from CT scans and MRIs, as well as whole-slide histopathology imaging. * **Longitudinal Review:** New capabilities allow for the review of chest X-ray time series, helping clinicians track disease progression over time. * **Anatomical Localization:** Developers can use the model to identify and localize specific anatomical features within chest X-rays. * **Document Understanding:** Enhanced support for extracting structured data from complex medical lab reports and documents. * **Edge Capability:** The 4B parameter size is specifically designed to be small enough to run offline while remaining accurate enough for core medical reasoning tasks. ### Medical Speech-to-Text with MedASR MedASR is a specialized automated speech recognition (ASR) model designed to bridge the gap between clinical dialogue and digital documentation. * **Clinical Dictation:** The model is specifically fine-tuned for medical terminology and the unique nuances of clinical dictation. * **Integrated Reasoning:** MedASR is designed to pair seamlessly with MedGemma, allowing transcribed text to be immediately processed for advanced medical reasoning or summarization. * **Accessibility:** Like other HAI-DEF models, it is free for research and commercial use and hosted on both Hugging Face and Google Cloud’s Vertex AI. ### Performance Benchmarks and Community Impact Google is incentivizing innovation through improved performance metrics and community-driven challenges. * **Accuracy Gains:** Internal benchmarks show MedGemma 1.5 improved disease-related CT classification by 3% and MRI classification by 14% compared to the previous version. * **MedGemma Impact Challenge:** A Kaggle-hosted hackathon with $100,000 in prizes has been launched to encourage developers to find creative applications for these multimodal tools. * **Model Collection:** The update complements existing tools like the MedSigLIP image encoder and the larger MedGemma 27B model, which remains the preferred choice for complex, text-heavy medical applications. Developers and researchers are encouraged to utilize MedGemma 1.5 for tasks requiring efficient, offline multimodal processing, while leveraging MedASR to automate clinical documentation. By participating in the MedGemma Impact Challenge, the community can help define the next generation of AI-assisted medical diagnostics and workflows.

AI Generative AI Multimodal AI Speech To Text+5

googleJan 11, 2026Original article

NeuralGCM harnesses AI to better simulate long-range global precipitation (opens in new tab)

NeuralGCM represents a significant evolution in atmospheric modeling by combining traditional fluid dynamics with neural networks to solve the long-standing challenge of simulating global precipitation. By training the AI component directly on high-quality NASA satellite observations rather than biased reanalysis data, the model achieves unprecedented accuracy in predicting daily weather cycles and extreme rainfall events. This hybrid approach offers a faster, more precise tool for both medium-range weather forecasting and multi-decadal climate projections. ## The Limitations of Cloud Parameterization * Precipitation is driven by cloud processes occurring at scales as small as 100 meters, which is far below the kilometer-scale resolution of global weather models. * Traditional models rely on "parameterizations," or mathematical approximations, to estimate how these small-scale events affect the larger atmosphere. * Because these approximations are often simplified, traditional models struggle to accurately capture the complexity of water droplet formation and ice crystal growth, leading to errors in long-term forecasts. ## Training on Direct Satellite Observations * Unlike previous AI models trained on "reanalyses"—which are essentially simulations used to fill observational gaps—NeuralGCM is trained on NASA satellite-based precipitation data spanning 2001 to 2018. * The model utilizes a differentiable dynamical core, an architecture that allows the neural network to learn the effects of small-scale events directly from physical observations. * By bypassing the weaknesses inherent in reanalysis data, the model effectively creates a machine-learned parameterization that is more faithful to real-world cloud physics. ## Performance in Weather and Climate Benchmarks * At a resolution of 280 km, NeuralGCM outperforms leading operational models in medium-range forecasts (up to 15 days) and matches the precision of sophisticated multi-decadal climate models. * The model shows a marked improvement in capturing precipitation extremes, particularly for the top 0.1% of rainfall events. * Evaluation through WeatherBench 2 demonstrates that NeuralGCM accurately reproduces the diurnal (daily) weather cycle, a metric where traditional physics-based models frequently fall short. NeuralGCM provides a highly efficient and accessible framework for researchers and city planners who need to simulate long-range climate scenarios, such as 100-year storms or seasonal agricultural cycles. Its ability to maintain physical consistency while leveraging the speed of AI makes it a powerful candidate for the next generation of global atmospheric modeling.

AI Machine Learning Neural Networks Climate Modeling+5

googleDec 17, 2025Original article

Google Research 2025: Bolder breakthroughs, bigger impact (opens in new tab)

Google Research in 2025 has shifted toward an accelerated "Magic Cycle" that rapidly translates foundational breakthroughs into real-world applications across science, society, and consumer products. By prioritizing model efficiency, factuality, and agentic capabilities, the organization is moving beyond static text generation toward interactive, multi-modal systems that solve complex global challenges. This evolution is underpinned by a commitment to responsible AI development, ensuring that new technologies like quantum computing and generative UI are both safe and culturally inclusive. ## Enhancing Model Efficiency and Factuality * Google introduced new efficiency-focused techniques like block verification (an evolution of speculative decoding) and the LAVA scheduling algorithm, which optimizes resource allocation in large cloud data centers. * The Gemini 3 model achieved state-of-the-art results on factuality benchmarks, including SimpleQA Verified and the newly released FACTS benchmark suite, by emphasizing grounded world knowledge. * Research into Retrieval Augmented Generation (RAG) led to the development of the LLM Re-Ranker in Vertex AI, which helps models determine if they possess sufficient context to provide accurate answers. * The Gemma open model expanded to support over 140 languages, supported by the TUNA taxonomy and the Amplify initiative to improve socio-cultural intelligence and data representation. ## Interactive Experiences through Generative UI * A novel implementation of generative UI allows Gemini 3 to dynamically create visual interfaces, web pages, and tools in response to user prompts rather than providing static text. * This technology is powered by specialized models like "Gemini 3-interactive," which are trained to output structured code and design elements. * These capabilities have been integrated into AI Mode within Google Search, allowing for more immersive and customizable user journeys. ## Advanced Architectures and Agentic AI * Google is exploring hybrid model architectures, such as Jamba-style models that combine State Space Models (SSMs) with traditional attention mechanisms to handle long contexts more efficiently. * The development of agentic AI focuses on models that can reason, plan, and use tools, exemplified by Project Astra, a prototype for a universal AI agent. * Specialized models like Gemini 3-code have been optimized to act as autonomous collaborators for software developers, assisting in complex coding tasks and system design. ## AI for Science and Planetary Health * In biology, research teams utilized AI to map human heart and brain structures and employed RoseTTAFold-Diffusion to design new proteins for therapeutic use. * The NeuralGCM model has revolutionized Earth sciences by combining traditional physics with machine learning for faster, more accurate weather and climate forecasting. * Environmental initiatives include the FireSat satellite constellation for global wildfire detection and the expansion of AI-driven flood forecasting and contrail mitigation. ## Quantum Computing and Responsible AI * Google achieved significant milestones in quantum error correction, developing low-overhead codes that bring the industry closer to a reliable, large-scale quantum computer. * Security and safety remain central, with the expansion of SynthID—a watermarking tool for AI-generated text, audio, and video—to help users identify synthetic content. * The team continues to refine the Secure AI Framework (SAIF) to defend against emerging threats while promoting the safe deployment of generative media models like Veo and Imagen. To maximize the impact of these advancements, organizations should focus on integrating agentic workflows and RAG-based architectures to ensure their AI implementations are both factual and capable of performing multi-step tasks. Developers can leverage the Gemma open models to build culturally aware applications that scale across diverse global markets.

AI Large Language Models Generative AI RAG+5

googleDec 14, 2025Original article

Gemini provides automated feedback for theoretical computer scientists at STOC 2026 (opens in new tab)

Google Research launched an experimental program for the STOC 2026 conference using a specialized Gemini model to provide automated, rigorous feedback on theoretical computer science submissions. By identifying critical logical errors and proof gaps within a 24-hour window, the tool demonstrated that advanced AI can serve as a powerful pre-vetting collaborator for high-level mathematical research. The overwhelmingly positive reception from authors indicates that AI can effectively augment the human peer-review process by improving paper quality before formal submission. ## Advanced Reasoning via Inference Scaling - The tool utilized an advanced version of Gemini 2.5 Deep Think specifically optimized for mathematical rigor. - It employed inference scaling methods, allowing the model to explore and combine multiple possible solutions and reasoning traces simultaneously. - This non-linear approach to problem-solving helps the model focus on the most salient technical issues while significantly reducing the likelihood of hallucinations. ## Structured Technical Feedback - Feedback was delivered in a structured format that included a high-level summary of the paper's core contributions. - The model provided a detailed analysis of potential mistakes, specifically targeting errors within lemmas, theorems, and logical proofs. - Authors also received a categorized list of minor corrections, such as inconsistent variable naming and typographical errors. ## Identified Technical Issues and Impact - The pilot saw high engagement, with over 80% of STOC 2026 submitters opting in for the AI-generated review. - The tool successfully identified "critical bugs" and calculation errors that had previously evaded human authors for months. - Survey results showed that 97% of participants found the feedback helpful, and 81% reported that the tool improved the overall clarity and readability of their work. ## Expert Verification and Hallucinations - Because the users were domain experts, they were able to act as a filter, distinguishing between deep technical insights and occasional model hallucinations. - While the model sometimes struggled to parse complex notation or interpret figures, authors valued the "neutral tone" and the speed of the two-day turnaround. - The feedback was used as a starting point for human verification, allowing researchers to refine their arguments rather than blindly following the model's output. ## Future Outlook and Educational Potential - Beyond professional research, 75% of surveyed authors see significant educational value in using the tool to train students in mathematical rigor. - The experiment's success has led to 88% of participants expressing interest in having continuous access to such a tool throughout their entire research and drafting process. The success of the STOC 2026 pilot suggests that researchers should consider integrating specialized LLMs early in the drafting phase to catch "embarrassing" or logic-breaking errors. While the human expert remains the final arbiter of truth, these tools provide a necessary layer of automated verification that can accelerate the pace of scientific discovery.

AI Large Language Models Generative AI Gemini+5

googleDec 11, 2025Original article

Spotlight on innovation: Google-sponsored Data Science for Health Ideathon across Africa (opens in new tab)

Google Research, in partnership with several pan-African machine learning communities, recently concluded the Africa-wide Data Science for Health Ideathon to address regional medical challenges. By providing access to specialized open-source health models and technical mentorship, the initiative empowered local researchers to develop tailored solutions for issues ranging from maternal health to oncology. The event demonstrated that localized innovation, supported by high-performance AI foundations, can effectively bridge healthcare gaps in resource-constrained environments. ## Collaborative Framework and Objectives * The Ideathon was launched at the 2025 Deep Learning Indaba in Kigali, Rwanda, in collaboration with SisonkeBiotik, Ro’ya, and DS-I Africa. * The primary goal was to foster capacity building within the African AI community, moving beyond theoretical research toward the execution of practical healthcare tools. * Participants received hands-on training on Google’s specialized health models and were supported with Google Cloud Vertex AI compute credits and mentorship from global experts. * Submissions were evaluated based on their innovation, technical feasibility, and contextual relevance to African health systems. ## Technical Foundations and Google Health Models * Developers focused on a suite of open health AI models, including MedGemma for clinical reasoning, TxGemma for therapeutics, and MedSigLIP for medical vision-language tasks. * The competition utilized a two-phase journey: an initial "Idea Development" stage where teams defined clinical problems and outlined AI approaches, followed by a "Prototype & Pitch" phase. * Technical implementations frequently involved advanced techniques such as Retrieval-Augmented Generation (RAG) to ensure alignment with local medical protocols and WHO guidelines. * Fine-tuning methods, specifically Low-Rank Adaptation (LoRA), were utilized by teams to specialize large-scale models like MedGemma-27B-IT for niche datasets. ## Innovative Solutions for Regional Health * **Dawa Health:** This first-place winner developed an AI-powered cervical cancer screening tool that uses MedSigLIP to identify abnormalities in colposcopy images uploaded via WhatsApp, combined with Gemini RAG for clinical guidance. * **Solver (CerviScreen AI):** This team built a web application for automated cervical-cytology screening by fine-tuning MedGemma-27B-IT on the CRIC dataset to assist cytopathologists with annotated images. * **Mkunga:** A maternal health call center that adapts MedGemma and Gemini to provide advice in Swahili using Speech-to-Text (STT) and Text-to-Speech (TTS) technologies. * **HexAI (DermaDetect):** Recognized for the best proof-of-concept, this offline-first mobile app allows community health workers to triage skin conditions using on-device versions of MedSigLIP, specifically designed for low-connectivity areas. The success of the Ideathon underscores the importance of "local solutions for local priorities." By making sophisticated models like MedGemma and MedSigLIP openly available, the technical barrier to entry is lowered, allowing African developers to build high-impact, culturally and linguistically relevant medical tools. For organizations looking to implement AI in global health, this model of providing foundational tools and cloud resources to local experts remains a highly effective strategy for sustainable innovation.

AI Machine Learning Generative AI RAG+5

googleDec 9, 2025Original article

A differentially private framework for gaining insights into AI chatbot use (opens in new tab)

Google Research has introduced Urania, a novel framework designed to extract high-level usage insights from AI chatbot conversations while maintaining rigorous differential privacy (DP) guarantees. Unlike previous heuristic methods that rely on simple redaction or LLM-based PII stripping, this pipeline ensures that no individual user's data can be reconstructed from the resulting summaries. By combining DP clustering and keyword extraction with LLM-based summarization, the system provides a formal, auditable approach to understanding platform trends without compromising sensitive information. ## Limitations of Heuristic Privacy * Existing frameworks often rely on large language models to manually strip personally identifiable information (PII) from text before analysis. * These heuristic protections are difficult to formalize or audit, and their effectiveness may diminish as models evolve or face sophisticated prompt injection attacks. * The Urania framework addresses these weaknesses by using mathematical privacy budgets (the epsilon parameter) to measure and limit the influence of any single user's data on the final output. ## The Differentially Private Pipeline * **DP Clustering**: The framework first converts conversation data into numerical embeddings. These are grouped using a DP clustering algorithm, ensuring that cluster centers reflect broad trends rather than specific individual inputs. * **DP Keyword Extraction**: The system identifies keywords for each cluster and generates a histogram of their frequency. By adding mathematical noise to these counts, the framework masks individual contributions and ensures that only keywords common to many users are retained. * **Keyword Generation Methods**: The researchers explored three methods for extraction: LLM-guided selection of relevant terms, a differentially private version of TF-IDF, and an LLM-guided approach that selects terms from a pre-defined list of public keywords. * **LLM Summarization**: In the final stage, an LLM generates a high-level summary of the cluster using only the noisy, anonymized keywords. Because the LLM never sees the raw conversation text, the "post-processing" property of DP guarantees that the final summary remains private. ## Privacy and Utility Trade-offs * The framework was tested against a non-private baseline (Simple-CLIO) to evaluate how privacy constraints affect the quality of the insights generated. * Stronger privacy settings (lower epsilon values) inherently result in a utility trade-off, as the added noise can obscure some niche usage patterns. * Despite these trade-offs, the framework provides a robust defense against data leakage, as the summarization model is structurally prevented from seeing sensitive original text, making it resilient to prompt injection. This framework offers a scalable way for platform providers to analyze chatbot usage patterns and enforce safety policies while providing mathematical certainty regarding user privacy. For organizations handling sensitive conversation data, moving from heuristic redaction to formal DP pipelines like Urania provides a more robust and auditable path for service improvement.

AI Large Language Models Differential Privacy Embeddings+3