Singapore

Recent developments on chest radiographs has primarily focused on developing multi-disease frameworks that aim to diagnose a wide range of thoracic abnormalities from the Chest X-ray datasets. In contrast, this study specifically targets pneumothorax, a life-threatening condition commonly referred to as a collapsed lung, which requires timely detection and accurate clinical reporting. Existing automated report generation Vision-Language Models (VLMs) mainly rely on image-level features and often fail to fully leverage the rich structural information embedded in medical image segmentation. To address this limitation, we propose a distinct strategy to incorporate pneumothorax segmentation masks, which delineate affected regions and provide precise localization guidance to enhance the accuracy of medical image interpretation. Experimental results demonstrate that the proposed segmentation-guided approach integrates visual and textual understanding more effectively for pneumothorax diagnosis from chest radiographs. By employing segmentation masks as guidance, VLMs can accurately localize pathological regions while preserving anatomical context, thereby improving both interpretability and diagnostic precision. Quantitative evaluations across multiple metrics further confirm the effectiveness of the proposed methods in bridging the gap between image-level localization and report-level reasoning.

AAAI 2026

Segmentation-Guided Radiology Report Generation for Pneumothorax Detection in Chest X-Rays

segmentation

radiology report generation

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Mammography is one of the primary imaging modalities for breast cancer screening and diagnosis, playing a pivotal role in early detection and mortality reduction. To alleviate the burden on radiologists interpreting mammographic data, artificial intelligence-based models have emerged as decision-making assistants, demonstrating promising results in several studies. However, a critical gap remains between AI capabilities and clinical integration. Current AI systems predominantly employ end-to-end classification approaches that bypass the structured, multi-step reasoning radiologists use in practice. Radiologists systematically detect abnormalities, characterize their features using standardized descriptors, correlate findings across imaging views, perform temporal comparisons, assess information sufficiency, and synthesize evidence into risk-stratified recommendations. In contrast, most AI models map directly from images to diagnoses without transparent intermediate reasoning, limiting their interpretability, clinical utility, and ability to generalize beyond training distributions. This paper examines the radiological chain-of-thought process in mammography interpretation, reviews current AI approaches and their limitations, and proposes a multi-stage reasoning framework that explicitly models each step of clinical decision-making. By decomposing the diagnostic task into sequential reasoning-based stages, this framework aims to create AI systems that not only predict outcomes but also reason transparently in alignment with clinical workflow. Crucially, we explain how contextual information such as breast density serves as a conditioning variable that dynamically optimizes subsequent reasoning stages, mirroring the adaptive decision-making process radiologists employ in clinical practice. We discuss the implications of this approach and identify critical dataset limitations that must be addressed to enable the development of truly reasoning-aware AI in breast imaging.

The Need to Move Beyond Explainability Toward Chain-of-Thought Reasoning: A Focus on AI for Mammography

Clinical ultrasound acquisition is highly operator-dependent, where rapid probe motion and brightness fluctuations often lead to reconstruction errors that reduce trust and clinical utility. We present UltrasODM, a dual-stream framework that assists sonographers during acquisition through calibrated per-frame uncertainty, saliency-based diagnostics, and actionable prompts. UltrasODM integrates (i) a contrastive ranking module that groups frames by motion similarity, (ii) an optical-flow stream fused with Dual Mamba temporal modules for robust 6-DoF pose estimation, and (iii) a Human-in-the-Loop (HITL) layer combining Bayesian uncertainty, clinician-calibrated thresholds, and saliency maps highlighting regions of low confidence. When uncertainty exceeds the threshold, the system issues unobtrusive alerts suggesting corrective actions such as re-scanning highlighted regions or slowing the sweep. Evaluated on a clinical freehand ultrasound dataset, UltrasODM reduces drift by 15.2%, distance error by 12.1%, and Hausdorff distance by 10.1% relative to UltrasOM, while producing per-frame uncertainty and saliency outputs. By emphasizing transparency and clinician feedback, UltrasODM improves reconstruction reliability and supports safer, more trustworthy clinical workflows.

UltrasODM: A Dual Stream Optical Flow Mamba Network for 3D Freehand Ultrasound Reconstruction

The rise in clinical AI has helped to define the diagnostic and decision-making process in the dimension of modern medicine. However, their implementation is constrained by multiple ethical, interpretability, and safety factors that hinder the effective utilization of these AI systems. Existing Human-in-the-Loop (HITL) systems rely heavily on externally triggered human oversight, which creates critical gaps in effective safety and accountability. This study introduces the AI-Instigated Human Oversight (AIHO) framework, an AI governance architecture that enables models to self-assess uncertainty or contextual failure and autonomously escalate decisions to human oversight through a four-layer detection and escalation mechanism. Each layer performs a distinct self-assessment: (Layer 1) predictive uncertainty quantification, (Layer 2) contextual validation and explainability, (Layer 3) ethical and proxy alignment monitoring, and (Layer 4) adaptive governance with human-in-command enforcement. These layers form part of a three-zone operational architecture: (1) Information Flow and AI Core Processing, (2) AIHO Oversight Core, and (3) Human Escalation Loops. These zones in conjunction create a continuous feedback loop which administers continuous self-evaluation, transforming ethical and technical anomalies into actionable human oversight triggers. AIHO establishes a dynamic pathway toward regulation-ready, self-aware clinical AI, aligning with the current international standards for trustworthy and accountable AI in medicine.

AI-Instigated Human Oversight: Rethinking Human-in-the-Loop Safety in Clinical AI

Recent advancements in deep learning have enabled expert-level performance in medical imaging for disease classification, but their black-box decision making processes limit trust in them and their wide-spread clinical deployment. While Explainable Artificial Intelligence (XAI) methods aim to bridge this gap, studies focus on 2D data or pre-processed research datasets that overlook the role of medical imaging pre-processing operations which is an essential component of real-world 3D medical imaging workflows. To address this limitation, we propose XIMED, a systematic and predictive model–centered framework for evaluating explainability under realistic medical pre-processing conditions for volumetric medical data. The framework integrates five volumetric pre-processing variants and ten post-hoc attribution methods, evaluated through three complementary criteria: Correctness, Contrastivity, and Completeness, which together evaluate explanation dependence on model input, internal structure, and output behavior. Across more than 300 experimental configurations, XIMED reveals that gradient-based methods, such as Integrated Gradients and Blur Integrated Gradients, provide the most consistent and model-aligned explanations, while noise-based approaches like SmoothGrad and VarGrad are less sensitive to model behavior. These findings emphasize the importance of clinically realistic evaluation pipelines for reliable explainability in 3D medical imaging.

XIME3D : A Systematic Framework for Evaluating Explainable AI in 3D Medical Imaging under CT Image Pre-processing Variations

Aortic stenosis (AS) remains a major cardiovascular challenge, where early ECG indicators often elude conventional diagnostic approaches. Despite the success of deep learning in AS detection, clinical translation is impeded by limited interpretability of model decisions. This study investigates how neural network architecture influences explainability and interpretability in AS classification. Three models—ResNet-18 (CNN-based), LSTM, and ViT-MAE—were trained on an open-access cardiomechanical signals dataset of 100 valvular heart disease patients. Using Integrated Gradients, all models achieved strong performance (ResNet-18: 97.23%, LSTM: 98.96%, ViT-MAE: 88.56%) but displayed distinct attention mechanisms: ResNet-18 utilized global ECG features, LSTM captured temporal QRS dependencies, and ViT-MAE emphasized T-wave and QT interval features. All models consistently attended to clinically meaningful regions aligned with known AS physiology. These results reveal that explainability is architecture-dependent and advocate for architectureaware interpretation strategies to foster transparency, reliability, and clinical trust in cardiovascular AI applications.

Architecture-Aware Explainability in ECG Analysis: A Case Study of Aortic Stenosis Detection with ResNet18, LSTM and ViT-MAE ECG

We study meal-level inference of the carbohydrate calorie ratio (CCR), the fraction of total calories attributable to net carbohydrates, from early postprandial glucose responses (PPGR) recorded by continuous glucose monitors (CGMs). Using the date-shifted CGMACROS v1.0.0 dataset, we introduce GlucoGrapher, a deployment-minded pipeline that (i) aligns PPGR around meal onset, (ii) performs sensor-aware fusion of Dexcom and Libre traces with explicit discordance detection and gating, (iii) applies a dual preprocessing view of the signal (absolute mg/dL and percent change relative to baseline) with per-subject PPGR standardization to mitigate inter-individual scale shifts, and (iv) combines heterogeneous, and sometimes missing—out-of-fold predictions using fold-wise, mask-aware nonnegative meta-learning with isotonic calibration to avoid leakage. We augment PPGR shape descriptors (peak, time-to-peak, IAUC windows, slopes, late-ratio features) with lightweight behavior (steps, heart rate) and compact subject context (BMI, HbA1c strata, selected fasting labs, and up to eight microbiome principal components). Evaluated with 5-fold GroupKFold by participant over meals, GlucoGrapher attains RMSE , , , and Pearson . Performance is consistent across HbA1c-defined strata (Healthy/PreDM/T2D), indicating robustness to baseline glycemic status. Ablations show that the mask-aware meta-ensemble provides a substantive lift over calibrated tree baselines, highlighting the value of reliability-aware sensor fusion, dual preprocessing, and leak-free calibration. Framing CCR as a bounded, interpretable target in enables actionable CGM-only feedback without perfect food logging, supporting retrospective coaching and prospective planning.

GlucoGrapher: Sensor-Aware CGM Fusion with Mask-Aware Meta-Ensembles for Predicting Carbohydrate Caloric Ratio from Postprandial Glucose

Radiology worklists are typically processed on a first-in, first-out (FIFO) basis, even when studies differ greatly in clinical urgency. We propose a pragmatic alternative: using calibrated probabilities of intracranial hemorrhage (ICH) to prioritize head CT exams for earlier reading. Using the public RSNA-ICH dataset, we train slice-level detectors, aggregate them to the exam level, apply post-hoc calibration, and feed these scores into a transparent discrete-event simulator of the reading queue. The simulator quantifies how triage benefit reduction in median time-to-read (TTR) for ICH; scales with classifier AUC, workload (arrival rate), staffing, prevalence, and calibration. Across realistic loads, score-based prioritization yields substantial TTR reductions for ICH with minimal delay to non-ICH studies. We release a configuration-driven, reproducible pipeline that translates AI risk scores into operational metrics (minutes saved), enabling safe and data-driven evaluation before PACS/RIS deployment.

Translating Classifier Scores into Clinical Impact: Calibrated Risk and Queueing Simulation for AI-Assisted Radiology Worklist Triage

Multimodal medical image fusion aims to integrate complementary information from different imaging modalities to enhance clinical diagnosis and surgical navigation. While deep learning-based approaches have significantly advanced fusion quality over traditional methods by leveraging powerful feature extraction, challenges such as blurring, noise, and artifacts persist in the fused results. To address these issues, we propose a novel fusion framework that incorporates an entropy-based attention module to emphasize salient image regions. Our architecture is designed in a multi-scale manner, utilizing an adaptive gating mechanism to effectively extract and combine salient features across different scales. Additionally, we introduce a Top-K token vision transformer to enable efficient global feature extraction while reducing computational overhead by restricting the context space. We further demonstrate the effectiveness of our fused representations in the downstream task of oocyte quality prediction, showing improved accuracy over individual focal images as well as over other approaches. Extensive experiments on diverse medical imaging datasets demonstrate that our method achieves competitive performance compared to state-of-the-art techniques, both quantitatively and visually. Ablation studies underscore the importance of each proposed component.

TAFIE: Transformer-Assisted Fusion with Integrated Entropy Attention for Multimodal Medical Imaging

Chest X-rays are a widely-used, cost-effective imaging modality for medical investigations; however, they encode distinctive biometric signatures that enable identification attacks. We introduce \emph{PrivDiff-Net}, a novel diffusion-based framework that addresses critical privacy vulnerabilities in chest X-rays through anatomical biometric features while preserving essential diagnostic utility for clinical applications. Our approach introduces two modules in a latent diffusion framework: (1) a Selective Attribute Suppression (SAS) module that removes sensitive identity cues using orthogonal projection in cross-attention, and (2) a Selective Privacy Guidance (SPG) loss that discourages identity features while preserving diagnostic information during diffusion. Quantitative results show that PrivDiff-Net achieves near-random identification (AUC: 47%) while maintaining high diagnostic accuracy (AUC: 78%). It effectively suppresses sensitive attributes and produces high-quality anonymized CXRs, validated by clinicians for diagnostic utility. These results establish \emph{PrivDiff-Net} as a new benchmark for privacy-preserving Chest X-rays, providing a practical solution for secure data sharing in collaborative research environments while enabling ethical deployment of AI systems in healthcare where transparency and patient privacy are critical.

Hide Identity, Preserve Pathology: Diffusion-Based Anonymization for Chest X-rays

Conversational AI is starting to support real clinical work, but most evaluation methods miss how compliance depends on the full course of a conversation. We introduce Obligatory-Information Phase Structured Compliance Evaluation (OIP–SCE), an evaluation method that checks whether every required clinical obligation is met, in the right order, with clear evidence for clinicians to review. This makes complex rules practical and auditable, helping close the gap between technical progress and what healthcare actually needs. We demonstrate the method in two case studies (respiratory history, benefits verification) and show how phase-level evidence turns policy into shared, actionable steps. By giving clinicians control over what to check and engineers a clear specification to implement, OIP–SCE provides a single, auditable evaluation surface that aligns AI capability with clinical workflow and supports routine, safe use.

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The Need to Move Beyond Explainability Toward Chain-of-Thought Reasoning: A Focus on AI for Mammography

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Evaluating Large Language Models for Pneumothorax Detection on Chest X-Rays

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Next from AAAI 2026

The Need to Move Beyond Explainability Toward Chain-of-Thought Reasoning: A Focus on AI for Mammography

Similar lecture

Evaluating Large Language Models for Pneumothorax Detection on Chest X-Rays

Stay up to date with the latest Underline news!

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