Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context

RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching

As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language model (LLM)-based metrics are now the best-correlated with radiologist judgment, yet they output a single opaque score that neither a clinician nor a model builder can easily interpret or audit. We introduce RadMatch, a multi-stage, LLM-based metric that decomposes report comparison into a structured finding-level matching with significance-aware scoring and error characterization across seven clinical attribute dimensions (status, location, severity, morphology, certainty, longitudinal comparison, and measurement). The main score is the actionable-error count, both interpretable and auditable. Candidate findings are graded correct, partial, or incorrect, and unmatched findings are counted as missed or hallucinated. Triage and actionable safety recall/precision and per-subset views add complementary, deployment-oriented lenses. Across two expert benchmarks, RadMatch is the most clinically aligned metric, matching inter-radiologist agreement on ReXVal and more than doubling the best prior metric on the harder RadEvalExpert. Relying only on few-shot prompting, it is designed to extend to other modalities and anatomies. We will release RadMatch as open-source code with an interactive dashboard for inspecting results.

PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation

Radiology report generation has matured almost entirely on 2D chest radiographs, where the default route to better reports is a larger backbone or a pre-training one on medical data. We revisit that assumption on 3D multi-sequence brain MRI, a volumetric multi-disease regime, and find that the model is not the lever. Zero-shot medical and radiology vision-language models transfer poorly to brain MRI, with chest radiograph specialists failing most conspicuously, and five backbones fine-tuned identically across three model families and an order of magnitude in scale differ only marginally. What determines the quality of the report is the information injected into the prompt. We delegate perception to upstream 3D segmentation and classification, serialize their outputs into a structured fact sentence, and prompt a LoRA-adapted vision-language model with it; we call this \textbf{PerFact}. In a controlled study that fixes the backbone, data split, target reports, and adaptation while varying only the injected grounding, perception-derived facts outperform retrieved prior reports, retrieval becomes redundant once facts are present, and end-to-end predicted facts remain effective without any ground-truth annotation at inference. The residual gap between predicted and oracle facts is explained by the granularity of the facts rather than by the generator. Closed-ended visual question answering comes at no measurable cost to report quality, though the grounding source has little effect on it. On 3D brain MRI, grounding information, not model choice, is the dominant controllable factor in report quality.

Context-Aware Filtering of Unstructured Radiology Reports by Anatomical Region

Radiology reports contain essential clinical information but often remain in unstructured, free-text formats. Notably, multiple imaging examinations performed simultaneously (such as CT head, facial bones, and cervical spine in trauma cases) may be bundled into a single report that consolidates findings from all studies into one free-text document, written jointly. Because individual sentences may reference ambiguous or overlapping anatomy (e.g., “there is a fracture”), sentence-level anatomic classification—filtering a report to retain only findings relevant to a specific anatomical region—is essential for downstream tasks such as structured label extraction and for creating clean, bijective training data for radiology report generation models. While formatting differs across reports, the clinical language remains precise. Using that fact, we develop context-aware classical models with feature engineering that surpass trained neural networks and pre-trained language models. We show that the learned model weights generalize effectively to MIMIC-IV radiology reports and that our approach achieves near-optimal performance with only a small amount of labeled training data. Together, these results make our approach practical and reproducible for new settings.

Patient-specific multimodal learning with multi-view contrastive alignment for chest X-ray report generation

Abstract Motivation Radiology reports play a pivotal role in guiding treatment planning and enabling effective doctor-patient communication. However, their manual composition imposes a substantial workload on radiologists. Although automatic radiology report generation has emerged as a promising alternative, existing approaches predominantly rely on single-view chest X-rays and fail to adequately leverage patient-specific context, thereby limiting diagnostic accuracy. Results To address this challenge, we propose EVOKE, a novel chest X-ray report generation framework that incorporates multi-view contrastive learning and patient-specific knowledge. Specifically, we introduce a multi-view contrastive learning method that captures semantic correspondences both among multi-view radiographs within a study and between these radiographs and their associated report, thereby improving visual representation learning. We further present a knowledge-guided report generation module that integrates available patient-specific knowledge (i.e. indication, which includes symptom descriptions) to facilitate the generation of accurate and coherent radiology reports. To support research in multi-view report generation, we construct Multi-view CXR and Two-view CXR datasets using publicly available sources. Our proposed EVOKE surpasses recent state-of-the-art methods across multiple datasets, achieving a 2.9% F1 RadGraph improvement on MIMIC-CXR, a 5.0% BLEU-1 improvement on MIMIC-ABN, a 1.5% BLEU-4 improvement on Multi-view CXR, and an 8.2% F1,mic-14 CheXbert improvement on Two-view CXR. Availability Code is publicly available at https://github.com/mk-runner/EVOKE, with an archived release available on Zenodo (doi:10.5281/zenodo.21000219).

Radiologist Detection of Deficiencies in LLM-Generated Patient Communications About Radiology Reports: A Multi-Reader Study.

From an Interesting Patient to a Publishable Paper

Radiological case reports are most useful when they enable readers to reconstruct the clinical question, inspect the decisive original imaging evidence, follow the diagnostic reasoning, and evaluate how the diagnosis was confirmed and what happened afterwards. CARE-radiology provides the radiology-specific reporting framework. This Educational Editorial & Practical Guide translates CARE-radiology, CARE, ICMJE, COPE, the 2024 Declaration of Helsinki and CRediT into a 12-step SJORANM workflow for authors, reviewers and editors. SJORANM requires original diagnostic radiological images as primary evidence and a dedicated Patient Perspective section (or a transparent explanation if unobtainable). Pathology is preferred when clinically relevant and available but is not mandatory when another credible reference standard exists. The framework distinguishes external reporting requirements, good scientific practice and journal-specific policy. Accompanying tools include author and reviewer checklists, a technical pre-check, standardised decision-letter modules and a CARE-radiology crosswalk. The aim is an educational, internally consistent and independently verifiable case report before peer review.