MedPAO-VL deploys an iterative plan-act-observe agent for structured radiology reports

Vaidya et al. introduce MedPAO-VL, replacing opaque end-to-end chest X-ray report generation with a plan-act-observe loop that iteratively reconciles findings against visual evidence.

Primary source: arXiv:2610.21794: https://arxiv.org/abs/2610.21794

What’s new: Despite advancements in vision-language models, generating structured and clinically accurate radiology reports from chest radiographs remains unreliable. Monolithic end-to-end models operate as opaque black boxes that frequently hallucinate non-existent pathology or overlook subtle visual abnormalities due to strong linguistic priors. In MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation (arXiv:2610.21794, 15 Oct 2026), Shrish Shrinath Vaidya, Gowthamaan Palani, Minmini Selvam, Gokulraja Srinivasaraja, and Ganapathy Krishnamurthi introduce MedPAO-VL, a goal-oriented multimodal agent structured around a plan-act-observe reasoning loop.

Instead of predicting an entire report in a single autoregressive pass, MedPAO-VL decomposes clinical report generation into a structured iterative workflow. First, the agent interrogates the input radiograph using an explainable vision module that models diagnostic uncertainty and detects localized pathological features. Next, the agent executes drafting actions according to standardized clinical reporting protocols. Crucially, an observation module evaluates intermediate text against the visual evidence, identifying semantic discrepancies, omissions, or unsupported claims. Through iterative self-correction, the agent refines its draft until the output aligns with detected findings. Radiologist evaluations confirm that MedPAO-VL significantly improves factual accuracy and protocol adherence, achieving an expert-rated clinical accuracy score of 3.5 out of 5 and a structural compliance score of 4.3 out of 5.

Why it matters: Clinical radiology workflows require strict adherence to structured templates and zero tolerance for fabricated findings. By replacing monolithic generative models with an explainable plan-act-observe loop, MedPAO-VL provides transparent clinical reasoning where every sentence is tied to verifiable visual evidence, bridging the gap between experimental vision-language models and institutional clinical reporting standards.

Caveats: The iterative plan-act-observe cycle requires multiple vision-language inference passes per radiograph, increasing inference latency relative to single-pass feedforward architectures. Additionally, clinical validation was conducted primarily on chest X-ray datasets, and extending the framework to complex 3D modalities such as contrast-enhanced CT or MRI will require volumetric observation modules.