Volumetric Radiology AI in the Era of Multimodal Large Language Models

A new review of more than 200 publications through July 2026 finds that current multimodal large language models (MLLMs) are poorly suited for volumetric radiology because they rely on 2D images or compressed representations, and proposes a Claim-Design-Validation framework to ensure clinical credibility. The review, published on arXiv, organizes the literature around volumetric representation, multimodal understanding, and agentic orchestration, and distinguishes tasks where 2D views suffice from those requiring native 3D modeling.

arXiv:2608.20549v1 Announce Type: new Abstract: Advances in multimodal large language models MLLMs are extending radiological artificial intelligence AI beyond task-specific image analysis toward multimodal understanding and reasoning. Volumetric radiology, however, presents a fundamental representational mismatch: clinical interpretation often requires full-volume spatial context and acquisition-dependent quantitative information, whereas current MLLMs are commonly conditioned on selected two-dimensional 2D images, compressed visual representations, or report-derived text. Reliable volumetric radiology AI therefore requires representations that preserve task-relevant three-dimensional 3D information and systems that can access, verify, and integrate this information across clinical workflows. In this Review, we examine more than 200 publications through July 2026. We organize the literature around volumetric representation and multimodal understanding at the model level, agentic orchestration at the system level, and their links to clinical applications and evaluation. We review volumetric foundation models, language alignment and compression strategies, and agentic systems that extend MLLMs through planning, tools, memory, and workflow interaction. We distinguish settings in which selected 2D views or report-mediated reasoning may suffice from those that warrant native volumetric modeling. We also introduce a Claim-Design-Validation framework to assess whether technical, workflow, and clinical claims are matched by appropriate design and validation. Across the literature, native volumetric modeling and agentic capabilities depend on the spatial, quantitative, contextual, and workflow requirements of the intended task. Clinical credibility requires faithful volumetric representation, traceable system behavior, claim-aligned validation, and clearly defined human oversight in realistic workflows.