# MedGemma 1.5 Technical Report

arXiv: https://arxiv.org/abs/2604.05081 · HTML: https://mlsanity.com/paper/2604.05081/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [MedGemma Technical Report](https://mlsanity.com/paper/2507.05201/) (2025)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding](https://mlsanity.com/paper/2501.18362/) (2025)
- [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023)
- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Advancing Multimodal Medical Capabilities of Gemini](https://mlsanity.com/paper/2405.03162/) (2024)
- [MAIRA-1: A specialised large multimodal model for radiology report generation](https://mlsanity.com/paper/2311.13668/) (2023)
- [EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries](https://mlsanity.com/paper/2402.16040/) (2024)
- [Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer](https://mlsanity.com/paper/1811.06497/) (2018)
- [AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset](https://mlsanity.com/paper/2411.15640/) (2024)
- [Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation](https://mlsanity.com/paper/2311.18260/) (2023)
- [CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understanding](https://mlsanity.com/paper/2502.20509/) (2025)
- [PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation](https://mlsanity.com/paper/2502.10536/) (2025)

## Built on by (most cited first, 30 of 45)

- [PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?](https://mlsanity.com/paper/2607.28318/) (2026)
- [Large Language Models Lack Temporal Awareness of Medical Knowledge](https://mlsanity.com/paper/2605.13045/) (2026)
- [Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles](https://mlsanity.com/paper/2605.22177/) (2026)
- [Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study](https://mlsanity.com/paper/2606.03693/) (2026)
- [Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability](https://mlsanity.com/paper/2606.08850/) (2026)
- [ReportQA: QA-Based Radiology Report Evaluation](https://mlsanity.com/paper/2606.15037/) (2026)
- [CheXpercept: A Benchmark for Evaluating Expert-Level Lesion Perception in Chest X-rays](https://mlsanity.com/paper/2606.21020/) (2026)
- [Auditing Evidence Use in Medical LLM Diagnosis](https://mlsanity.com/paper/2607.20848/) (2026)
- [RadSight: Towards Perceptually Reliable Multimodal Radiology Image Understanding](https://mlsanity.com/paper/2607.22293/) (2026)
- [Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation](https://mlsanity.com/paper/2608.08713/) (2026)
- [RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching](https://mlsanity.com/paper/2609.01470/) (2026)
- [ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction](https://mlsanity.com/paper/2609.21755/) (2026)
- [The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models](https://mlsanity.com/paper/2604.14363/) (2026)
- [Rethinking Patient Education as Multi-turn Multi-modal Interaction](https://mlsanity.com/paper/2604.14656/) (2026)
- [EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records](https://mlsanity.com/paper/2605.12335/) (2026)
- [How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology](https://mlsanity.com/paper/2606.12407/) (2026)
- [Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis](https://mlsanity.com/paper/2606.17115/) (2026)
- [TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization](https://mlsanity.com/paper/2606.18312/) (2026)
- [Towards Real-World Ultrasound Understanding: Large Vision-Language Models from Multi-Image Examinations with Long-Form Reports](https://mlsanity.com/paper/2607.01908/) (2026)
- [Objective-Aligned Direct Answer SFT for Robust Multi-Frame Medical VQA](https://mlsanity.com/paper/2607.27566/) (2026)
- [MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation](https://mlsanity.com/paper/2608.00195/) (2026)
- [A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation](https://mlsanity.com/paper/2608.02805/) (2026)
- [Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination](https://mlsanity.com/paper/2608.05391/) (2026)
- [ELICITED: EHR-grounded Longitudinal Interactive Conversations for Information-seeking Triage Evaluation and Decision-making](https://mlsanity.com/paper/2608.09024/) (2026)
- [PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts](https://mlsanity.com/paper/2608.17607/) (2026)
- [Volumetric Radiology AI in the Era of Multimodal Large Language Models](https://mlsanity.com/paper/2608.20549/) (2026)
- [An end-to-end-trained vision-language model for native-language prostate pathology report generation](https://mlsanity.com/paper/2608.23143/) (2026)
- [CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation](https://mlsanity.com/paper/2608.30758/) (2026)
- [MedQA-MM: Shortcuts Behind Medical Visual Reasoning](https://mlsanity.com/paper/2609.03261/) (2026)
- [Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models](https://mlsanity.com/paper/2609.06419/) (2026)

## Abstract (arXiv)

We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imaging (CT/MRI volumes and histopathology whole slide images), anatomical localization via bounding boxes, multi-timepoint chest X-ray analysis, and improved medical document understanding (lab reports, electronic health records). We detail the innovations required to enable these modalities within a single architecture, including new training data, long-context 3D volume slicing, and whole-slide pathology sampling. Compared to MedGemma 1 4B, MedGemma 1.5 4B demonstrates significant gains in these new areas, improving 3D MRI condition classification accuracy by 11% and 3D CT condition classification by 3% (absolute improvements). In whole slide pathology imaging, MedGemma 1.5 4B achieves a 47% macro F1 gain. Additionally, it improves anatomical localization with a 35% increase in Intersection over Union on chest X-rays and achieves a 4% macro accuracy for longitudinal (multi-timepoint) chest x-ray analysis. Beyond its improved multimodal performance over MedGemma 1, MedGemma 1.5 improves on text-based clinical knowledge and reasoning, improving by 5% on MedQA accuracy and 22% on EHRQA accuracy. It also achieves an average of 18% macro F1 on 4 different lab report information extraction datasets (EHR Datasets 2, 3, 4, and Mendeley Clinical Laboratory Test Reports). Taken together, MedGemma 1.5 serves as a robust, open resource for the community, designed as an improved foundation on which developers can create the next generation of medical AI systems. Resources and tutorials for building upon MedGemma 1.5 can be found at https://goo.gle/medgemma.