Technical Developments in Radiology: Enhancing Research Workflows with PACScrawler.

RadHarmony: Radiological Data Handling in the Era of Agentic AI

Training deep learning models on radiological images requires integrating heterogeneous datasets across different sources, file formats, directory layouts, label schemas, and annotation types. We present RadHarmony, an open-source Python library that provides a unified API for loading, harmonizing, and augmenting radiological datasets, with a primary focus on chest radiographs and early support for computed tomography (CT) and magnetic resonance imaging (MRI). RadHarmony standardizes metadata from 24 public datasets into a single tabular format, wraps MONAI's map-style datasets for deep-learning-ready sample delivery with optional on-disk caching, and supports classification labels, segmentation masks, bounding boxes, and radiology report text through a single interface, with an interactive visualization tool for dataset exploration and verification. To lower the barrier for integrating new datasets, RadHarmony introduces an AI-agent skill that guides the full integration workflow from raw data inspection through code generation and testing. We demonstrate the library's utility by pretraining RadHarmony-ViT, a reference vision transformer baseline that combines three heterogeneous chest radiograph datasets with no dataset-specific code. The code and pretrained model weights are available at https://github.com/f10409/RadHarmony.

A Bounded Public DICOM Metadata Audit for UDI-DICOM Evidence Readiness in Medical Imaging Workflows.

TRANSFORMING RADIOLOGY WORKFLOW WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE REVIEW

Background: Artificial intelligence (AI) is increasingly being incorporated into radiology, not only for image interpretation but also for scheduling, examination protocoling, image acquisition, reconstruction, worklist prioritisation, quantitative analysis, reporting, communication, and follow-up. The clinical value of these systems depends on more than algorithmic accuracy. It also depends on interoperability, usability, external validation, human oversight, institutional readiness, and the ability to demonstrate measurable improvement in patient care. Objective: This review examines how AI is reshaping radiology workflow, summarises clinically relevant applications across imaging modalities, evaluates evidence regarding diagnostic performance and operational efficiency, and discusses implementation, ethics, regulation, workforce, economic, and equity-related considerations. Methods: A structured narrative review framework was developed using PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, PubMed Central, major radiology journals, and publicly available regulatory and professional sources. Original clinical research articles published between January 2016 and August 2026 were prioritised. Studies were considered when they evaluated an AI application in clinical imaging, measured diagnostic or workflow outcomes, or described prospective implementation. Because the studies differed substantially in task, population, modality, endpoint, and reference standard, findings were synthesised narratively rather than pooled statistically. Results: AI has demonstrated value in selected tasks involving mammographic screening, chest-radiograph interpretation, CT triage, MRI reconstruction, segmentation, quantitative imaging, and worklist prioritisation. Prospective and randomised studies suggest that AI can preserve or improve diagnostic performance while reducing selected forms of reader workload. Nevertheless, reported workflow gains are variable. Benefits may be attenuated by false-positive alerts, additional review requirements, poor system integration, case-mix differences, and local staffing patterns. Evidence connecting AI deployment with improved patient outcomes, cost-effectiveness, and long-term equity remains less mature. Conclusions: AI should be treated as a sociotechnical intervention rather than as a stand-alone software product. The most defensible implementation strategy is to begin with narrowly defined clinical problems, conduct local validation, integrate outputs into existing systems, train users, monitor performance after deployment, and retain accountable human oversight. Sustainable transformation will require prospective multicentre research, transparent reporting, interoperable architectures, lifecycle regulation, and deliberate protection against bias and unequal access.

RADAR: A Difference-Aware Retrieval with Organ-Level Alignment for Change Radiology Report Generation.

Automated report generation is limited to static, single-image analysis, failing to address the critical clinical need for longitudinal comparison in monitoring disease progression and treatment efficacy. To bridge this gap, we introduce the new task of Change Radiology Report Generation (CRRG) which aims to automatically generate a comparative radiology report describing interval changes between a patient's current and prior radiological images. To address this challenge, we propose RADAR, a novel framework that integrates deep visual comparison with knowledge-rich text generation. For robust visual analysis, RADAR employs an "align first, then compare" strategy via an organ-level alignment module, to first mitigate non-pathological artifacts before precisely identifying key pathological changes. Our text generation framework integrates soft prompts for visual evidence with a structured four-step workflow for clinical reasoning. Factual accuracy is enhanced by our Knowledge-Infused Generation (KIG) component, which dynamically retrieves knowledge from a database of similar pathological cases. To anchor our newly proposed task of CRRG and facilitate robust evaluation, we introduce the first comprehensive benchmark, featuring a meticulously curated and processed dataset. Extensive experiments on our benchmark demonstrate that RADAR outperforms existing methods on most evaluation metrics. Our work successfully elevates automated radiological assessment from static analysis to dynamic monitoring, producing interpretable and clinically relevant change reports for longitudinal comparison.

Agentic artificial intelligence in radiology workflow: from image interpretation to report quality control

Radiology AI has grown past the single-purpose detector. The newest systems, built around large language models (LLMs), chain together the steps a radiologist actually works through: triaging the worklist, retrieving prior imaging studies, processing images, drafting a structured report, checking for mistakes. When these modules are coordinated through an orchestration layer, they form a multi-agent system capable of managing multiple stages of the radiology workflow—software that handles stretches of the radiology pipeline with less human input at each stage. This review maps the evidence behind that shift, drawing on PubMed-indexed studies from 2023 to 2026. We begin with convolutional neural networks and foundation models, then follow the emergence of AI agents that observe, plan, and act inside clinical environments. We examine multi-agent architectures—specialized agents for image analysis, report drafting, error detection, and decision support—and ask what they actually deliver. So far, the data tell a consistent story: multi-agent cross-verification drives hallucination rates down; intelligent worklist triage cuts report turnaround time by up to 43.7% in some settings; GPT-4 catches 82.7% of report errors, matching human readers. But nearly all of this evidence comes from single-center, retrospective studies on curated data. Every systematic review reaches the same conclusion: the technology works in the lab and has not been proven in the clinic. We also discuss compound opacity—how layered agent interactions make decisions harder to trace—alongside poor reporting standards and a regulatory framework that was not designed for generative, continuously-adaptive software. Agentic AI, including multi-agent architectures, could reshape how radiology departments operate, but the field needs prospective, multi-center trials with standardized endpoints before claiming it already has.

Development and Real-World Evaluation of an NLP-Assisted Quality Control System for Radiology Reports.