Longitudinal Medical Image Analysis

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Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Apr 17, 2026Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan +3Medical Image Analysis Longitudinal Medical Image Analysis
Department of Radiation Oncology, TUM University Hospital, Munich, Germany · Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany · Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany +3
CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder
Apr 17, 2026Duy-Phuong Dao, Muhammad Taqiyuddin, Jahae Kim +4Latent Diffusion Models Longitudinal Medical Image Analysis
Department of Artificial Intelligence Convergence, Chonnam National University, South Korea · Department of Nuclear Medicine, Chonnam National University, South Korea · University of South Australia, Australia +2
SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
Mar 20, 2026Ruiying Chen, Yutong Wang, Houliang Zhou +3Irregular Time-Series Modeling Alzheimer's Disease
Lehigh University, Bethlehem, PA, USA · New York University, New York, NY, USA · University of Pennsylvania, Philadelphia, PA, USA
NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis
Mar 16, 2026James Song, Yifan Wang, Chuan Zhou +1Medical Image Analysis Longitudinal Medical Image Analysis
University of Michigan · University of Michigan Medical School
Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
Mar 5, 2026Zahra Karimaghaloo, Dumitru Fetco, Haz-Edine Assemlal +2Diffusion Models for Image Restoration Diffusion Model Guidance
Clario part of Thermofisher Scientific, Montreal, Canada · Concordia University, Montreal, Canada
Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling
Jan 21, 2026Cheng Wan, Bahram Jafrasteh, Ehsan Adeli +2Latent Diffusion Models Longitudinal Medical Image Analysis
Cornell University · Weill Cornell Medicine · Stanford University +1
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Dec 29, 2025Taha Emre, Arunava Chakravarty, Thomas Pinetz +9Masked Autoencoders Self-Supervised Pre-Training
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Department of Ophthalmology and Optometry, Medical University of Vienna, Austria · BioMedIA, Department of Computing, Imperial College London, London, United Kingdom +8
Longitudinal Risk Prediction in Mammography with Privileged History Distillation
Date pendingBanafsheh Karimian, Soufiane Belharbi, Alexis Guichemerre +3Breast Cancer Medical Imaging
LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada · Goodman Cancer Institute, Dept. of Oncology, McGill University, Canada

Apr 17, 2026, cs.CVJ/K move · Enter open · S save

Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis

Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan, Benedikt Wiestler+2

Department of Radiation Oncology, TUM University Hospital, Munich, Germany · Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany · Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany+3

Medical Image Analysis Longitudinal Medical Image Analysis LLM Tool Use Multi-Agent LLMs Medical Image Benchmarks Brain MRI+1

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State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation remains: current architectures lack the native 3D spatial reasoning required to directly analyze volumetric medical imaging, such as CT or MRI. Emerging agentic AI offers a new solution, eliminating the need for intrinsic 3D processing by enabling LLMs to orchestrate and leverage specialized external tools. Yet, the feasibility of such agentic frameworks in complex, multi-step radiological workflows remains underexplored. In this work, we present a training-free agentic pipeline for automated brain MRI analysis. Validating our methodology on several LLMs (GPT-5.4, Gemini 3.1 Pro, Claude Sonnet 4.6) with off-the-shelf domain-specific tools, our system autonomously executes complex end-to-end workflows, including preprocessing (skull stripping, registration), pathology segmentation (glioma, meningioma, metastases), and volumetric reasoning. We evaluate our framework across increasingly complex radiological tasks, from single-scan tumor and anatomy analysis to longitudinal response assessment requiring multi-timepoint comparisons. We analyze the impact of architectural design by comparing single-agent models against multi-agent "domain-expert" collaborations. To support rigorous evaluation of future agentic systems, we release a benchmark dataset of image-prompt-answer tuples derived from public data. Our results demonstrate that agentic AI can solve highly neuro-radiological image analysis tasks through tool use without the need for training or fine-tuning.

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