Capabilities of Gemini Models in Medicine

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   MedGemma Technical Report

   Andrew B. Sellergren, S. Kazemzadeh, Tiam Jaroensri et al. · 2025 · cité 532 fois · Pas encore résumé

   Résumé scientifique

   Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks, and the need to preserve privacy. Foundation models that perform well on medical tasks and require less task-specific tuning data are critical to accelerate the development of healthcare AI applications. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3 4B and 27B. MedGemma demonstrates advanced medical understanding and reasoning on images and text, significantly exceeding the performance of similar-sized generative models and approaching the performance of task-spe…

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   Application of Gemini in Public Health Amid the Artificial Intelligence Era

   Moosa Tatar, Soheila Farokhi, Arash Azizian Foumani et al. · 2026 · IEEE Engineering Management Review · cité 4 fois · Pas encore résumé

   Résumé scientifique

   In December 2023, Google DeepMind unveiled its large language model, Gemini, which is shown to be capable of understanding and processing multimodal information. This article explores the applications of Gemini in public health. Unlike traditional models, Gemini integrates and analyzes information through diverse modalities (text, image, video, etc.) fostering new applications. Notably, Google Gemini is presented as the first model to surpass human experts on the Massive Multitask Language Understanding benchmark, which combines multiple subjects including science, technology, engineering, and mathematics, humanities, and others to test the knowledge and problem-solving abilities of artifici…

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   Potential of multimodal large language models for data mining of medical images and free-text reports

   Yutong Zhang, Yi Pan, Tianyang Zhong et al. · 2024 · Meta-Radiology · cité 52 fois · Pas encore résumé

   Résumé scientifique

   Medical images and radiology reports are essential for physicians to diagnose medical conditions . However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (i…

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   An open vision-language model for diverse medical applications

   Andrew B. Sellergren, S. Kazemzadeh, Fereshteh Mahvar et al. · 2026 · Nature Medicine · Pas encore résumé

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   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. MedGemma demonstrates advanced medical understanding and reasoning across images and text and multiple medical imaging domains, exceeding the performance of similarly sized generative models while maintaining the general capab…

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   Towards Generalist Biomedical AI

   Tao Tu, Shekoofeh Azizi, Danny Driess et al. · 2023 · Computation and Language · preprint, non relu par les pairs · cité 647 fois · Pas encore résumé

   Résumé scientifique

   Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To enable the development of these models, we first curate MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduce Med-PaLM Multimodal (Med-PaLM M), ou…

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   A Modular Multi-Agent Multimodal AI Framework Integrating Vision–Language Models and IoT Data for Explainable Clinical Decision Support

   Et-Tousy Jamal, Chtara Malek, Zyane Abdellah · 2026 · 2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · Pas encore résumé

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   Clinical decision support systems now provide more options because of the integration of medical imaging, clinical text, and contextual data utilizing contemporary visionlanguage models (VLMs). Performance, explainability, accessibility, and regulatory compliance are all trade-offs in any model. In this study, we assess four VLMs using multimodal medical benchmarks: MedGemma-4B-IT, LLaVA-Med, MedVLMR1, and GPT-4V. Because of its high multimodal capabilities, reasoning ability, integration of external knowledge, and modifiability, we have chosen MedGemma-4B-IT as the best opensource choice. MedGemma-4B-IT provides competitive baseline performance on standard medical VQA benchmarks by building…

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   Towards conversational artificial intelligence for disease management

   Valentin Liévin, Anil Palepu, Wei-Hung Weng et al. · 2026 · Nature · cité 18 fois · Pas encore résumé

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   Although large language models have shown promise in diagnostic dialogue1, their capabilities for effective management reasoning, including disease progression, therapeutic response and safe medication prescription, have remained underexplored. We have advanced the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE)1, 2–3 using a new large-language-model-based agentic system optimized for multivisit clinical management and dialogue. To ground the reasoning of AMIE in authoritative clinical knowledge, we leveraged the long-context capabilities of Gemini4, combining in-context retrieval with structured reasoning to align its output with up-to-…

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   Meissa: Multi-modal Medical Agentic Intelligence

   Yi-Xiong Chen, Xin-Yi Bai, Yue Pan et al. · 2026 · arXiv.org · preprint, non relu par les pairs · cité 6 fois · Pas encore résumé

   Résumé scientifique

   Multi-modal large language models (MM-LLMs) have shown strong performance in medical image understanding and clinical reasoning. Recent medical agent systems extend them with tool use and multi-agent collaboration, enabling complex decision-making. However, these systems rely almost entirely on frontier models (e.g., GPT), whose API-based deployment incurs high cost, high latency, and privacy risks that conflict with on-premise clinical requirements. We present Meissa, a lightweight 4B-parameter medical MM-LLM that brings agentic capability offline. Instead of imitating static answers, Meissa learns both when to engage external interaction (strategy selection) and how to execute multi-step i…

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   Large Language Models lack essential metacognition for reliable medical reasoning

   Maxime Griot, Coralie Hemptinne, Jean M. Vanderdonckt et al. · 2025 · Nature Communications · cité 126 fois · Pas encore résumé

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   Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions. We evaluated twelve models on dimensions including confidence-based accuracy, missing answer recall, and unknown recall. Despite high accuracy on multiple-choice questions, our study revealed significant metacognitive deficiencies across all tested models. Models consistently failed to rec…

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   Multimodal generative AI for interpreting 3D medical images and videos

   Jung-Oh Lee, Hong-Yu Zhou, T. Berzin et al. · 2025 · npj Digital Medicine · cité 27 fois · Pas encore résumé

   Résumé scientifique

   This perspective proposes adapting video-text generative AI to 3D medical imaging (CT/MRI) and medical videos (endoscopy/laparoscopy) by treating 3D images as videos. The approach leverages modern video models to analyze multiple sequences simultaneously and provide real-time AI assistance during procedures. The paper examines medical imaging’s unique characteristics (synergistic information, metadata, and world model), outlines applications in automated reporting, case retrieval, and education, and addresses challenges of limited datasets, benchmarks, and specialized training.

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   Advancing conversational diagnostic AI with multimodal reasoning

   Khaled Saab, Chunjong Park, Tim Strother et al. · 2026 · Nature Medicine · cité 9 fois · Pas encore résumé

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   Real-world clinical practice is inherently multimodal, relying on the synthesis of patient history with visual information such as medical imagery and clinical documents. Although large language models (LLMs) have shown promise in diagnostic dialogue, their evaluation has been largely restricted to text-only interactions, failing to capture the complexity of modern remote care delivery. Here we introduce a multimodal extension of the Articulate Medical Intelligence Explorer (multimodal AMIE), capable of gathering, interpreting and reasoning about multimodal data within a diagnostic conversation. To achieve this, we developed a state-aware dialogue framework that dynamically guides history-ta…

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   Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge

   Mohammad R. Rezaei, Reza Saadati Fard, J. Parker et al. · 2025 · Conference on Empirical Methods in Natural Language Processing · cité 29 fois · Pas encore résumé

   Résumé scientifique

   Large Language Models (LLMs) have significantly advanced medical question-answering by leveraging extensive clinical data and medical literature. However, the rapid evolution of medical knowledge and the labor-intensive process of manually updating domain-specific resources pose challenges to the reliability of these systems. To address this, we introduce Agentic Medical Graph-RAG (AMG-RAG), a comprehensive framework that automates the construction and continuous updating of medical knowledge graphs, integrates reasoning, and retrieves current external evidence, such as PubMed and WikiSearch. By dynamically linking new findings and complex medical concepts, AMG-RAG not only improves accuracy…

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   Foundation Model for Medical Imaging: A Comprehensive Review

   Li-Cheng Jiao, Jingyi Yang, Rui-Yang Li et al. · 2026 · IEEE Transactions on Artificial Intelligence · cité 8 fois · Pas encore résumé

   Résumé scientifique

   The rapid development of highly flexible and reusable foundation models has introduced a new paradigm shift in the field of medical artificial intelligence (AI). Medical foundation models (MFMs) are built upon large-scale, diverse datasets and utilize multistage training strategies to thoroughly understand task representations, fitting the differences between various patterns, thereby achieving contextual reasoning abilities and broad applicability. MFMs can perform zero-shot inference on a variety of downstream tasks and also enhance the understanding of new tasks with task-specific prompts, without the need for extensive labeled data and retraining. To achieve more transparent and universa…