Google Research at Google I/O 2025 (opens in new tab)

Google Research at I/O 2025 showcases the "research to reality" transition, highlighting how years of foundational breakthroughs are now being integrated into Gemini models and specialized products. By focusing on multimodal capabilities, pedagogy, and extreme model efficiency, Google aims to democratize access to advanced AI while ensuring it remains grounded and useful across global contexts.

Specialized Healthcare Models: MedGemma and AMIE

• MedGemma: This new open model, based on Gemma 3, is optimized for multimodal medical tasks such as radiology image analysis and clinical data summarization. It is available in 4B and 27B sizes, performing similarly to much larger models on the MedQA benchmark while remaining small enough for efficient local fine-tuning.
• AMIE (Articulate Medical Intelligence Explorer): A research AI agent designed for diagnostic medical reasoning. Its latest multimodal version can now interpret and reason about visual medical information, such as skin lesions or medical imaging, to assist clinicians in diagnostic accuracy.

Educational Optimization through LearnLM

• Gemini 2.5 Pro Integration: The LearnLM family of models, developed with educational experts, is now integrated into Gemini 2.5 Pro. This fine-tuning enhances STEM reasoning, multimodal understanding, and pedagogical feedback.
• Interactive Learning Tools: A new research-optimized quiz experience allows students to generate custom assessments from their own notes, providing specific feedback on right and wrong answers rather than just providing solutions.
• Global Assessment Pilots: Through partnerships like the one with Kayma, Google is testing the automatic assessment of short and long-form content in regions like Ghana to scale quality educational tools.

Multilingual Expansion and On-Device Gemma Models

• Gemma 3 and 3n: Research breakthroughs have expanded Gemma 3’s support to over 140 languages. The introduction of Gemma 3n targets extreme efficiency, capable of running on devices with as little as 2GB of RAM while maintaining low latency and low energy consumption.
• ECLeKTic Benchmark: To assist the developer community, Google introduced this novel benchmark specifically for evaluating how well large language models transfer knowledge across different languages.

Model Efficiency and Factuality in Search

• Inference Techniques: Google Research continues to set industry standards for model speed and accessibility through technical innovations like speculative decoding and cascades, which reduce the computational cost of generating high-quality responses.
• Grounded Outputs: Significant focus remains on factual consistency, ensuring that the AI models powering features like AI Overviews in Search provide reliable and grounded information to users.

As Google continues to shrink the gap between laboratory breakthroughs and consumer products, the emphasis remains on making high-performance AI accessible on low-cost hardware and across diverse linguistic landscapes. Developers and researchers can now leverage these specialized tools via platforms like HuggingFace and Vertex AI to build more targeted, efficient applications.