Semantic de-identification of burned-in PHI in DICOM medical images: a deep learning–NLP pipeline validated on clinical and phantom TMM datasets

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

Masking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI

ClinX is introduced, an end-to-end multimodal PHI sanitization framework for medical image-text data, and results show that OCR-only masking is not sufficient as a standalone solution, and restoration-based sanitization better preserves clinically relevant visual context while sharply reducing recoverable PHI.

Dual U-shaped cross-modal fusion network for lung infection region segmentation

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.

SIFTING: A Novel LLM-Based Framework for Structured and Transparent Information Extraction from Clinical Free-Text Reports, with Application to Tumor Staging in Lung Cancer

Background: Large language models (LLMs) show promise for extracting information from clinical free-text documents, but their outputs are often unstructured and lack traceability, complicating validation and adoption in clinical workflows. In this work we introduce SIFTING, an LLM-based framework designed to address these shortcomings. Methods: SIFTING combines the language comprehension capabilities of LLMs with segment-level processing and structured prompts with strict output control, linking findings to the source text to enable both accurate and transparent information extraction. To demonstrate its capabilities, we applied the framework to the task of extracting tumor T-stage information from 130 lung cancer radiology reports (SIFTING-T-stage). A compact 4-bit quantized version of the open-source LLM Llama-3.3-70B (35 GB) was used in a fully self-hosted setup, providing full control over data and model. Performance was evaluated against a reference standard created by four clinical experts and compared with a range of LLMs as used in a conventional single-prompt approach, using bootstrap resampling to estimate confidence intervals. Results: SIFTING-T-stage achieved an accuracy of 90% (95% CI: 84-95) against the reference standard. We found its performance to be comparable to even the largest state-of-the-art LLMs with reasoning capabilities and to be interchangeable with clinical experts (p<0.001), while at the same time offering full traceability through source text references. Conclusion: SIFTING enables accurate, structured, and traceable information extraction from clinical free-text documents. It ensures data control, reproducibility, and verifiable outputs that can support clinical validation and workflow integration.

MedXAIgnosis: a metadata-enhanced graph-fusion pilot for thorax disease classification

Classifying medical images is essential for the diagnosis of thorax diseases, often aided by deep learning (DL) techniques. However, traditional DL approaches typically focus on a single image type input and overlook valuable insights from clinical header data. To address this, MedXAIgnosis introduces a multimodal framework that combines chest radiograph images and available clinical data (age, sex, view-position) to reorganize image features without extra clinician typing. The framework utilizes DenseNet to extract features from chest radiographs and a Multilayer Perceptron (MLP) to map clinical header data fields into a compatible space. These features are fused and passed to a Graph Neural Network (GNN) equipped with a learnable edge threshold that prunes noisy relations on-the-fly. On the original NIH and CheXpert test sets, the plug-in reaches an AUC of 0.8078 and an accuracy of 0.8425 (NIH) and an AUC of 0.7891 with an accuracy of 0.7997 (CheXpert), which is within the same range as other metadata-only models. For model transparency, explainable AI (XAI) techniques Gradient-weighted Class Activation Mapping (Grad-CAM) and GNNExplainer are implemented. Grad-CAM highlights the most relevant pixel regions in chest X-ray images, while GNNExplainer identifies influential subgraph patterns in the GNN, outputting a compact explanation that clinicians can inspect at a glance.