Singapore

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.

AAAI 2026

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

freehand imaging

mamba network

dual-stream architecture

3d ultrasound reconstruction

human-in-the-loop (hitl)

optical flow

contrastive learning

deep learning

Congratulations to all the researchers who have qualified for the AMA Research Challenge virtual poster symposium and semifinals!

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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.

All Required, In Order: Phase-Level Evaluation for AI–Human Dialogue in Healthcare and Beyond

Semi-supervised learning (SSL) has become a promising direction for medical image segmentation, enabling models to learn from limited labeled data alongside abundant unlabeled samples. However, existing SSL approaches for multi-modal medical imaging often struggle to exploit the complementary information between modalities due to semantic discrepancies and misalignment across MRI sequences. To address this, we propose a novel semi-supervised multi-modal framework that explicitly enhances modality-specific representations and facilitates adaptive cross-modal information fusion. Specifically, we introduce a Modality-specific Enhancing Module (MEM) to strengthen semantic cues unique to each modality via channel-wise attention, and a learnable Complementary Information Fusion (CIF) module to adaptively exchange complementary knowledge between modalities. The overall framework is optimized using a hybrid objective combining supervised segmentation loss and cross-modal consistency regularization on unlabeled data. Extensive experiments on the BraTS 2019 (HGG subset) demonstrate that our method consistently outperforms strong semi-supervised and multi-modal baselines under 1%, 5%, and 10% labeled data settings, achieving significant improvements in both Dice and Sensitivity scores. Ablation studies further confirm the complementary effects of our proposed MEM and CIF in bridging cross-modality discrepancies and improving segmentation robustness under scarce supervision.

Modality-Specific Enhancement and Complementary Fusion for Semi-Supervised Multi-Modal Brain Tumor Segmentation

Recent advances in foundation models have enabled their integration into high-stakes clinical settings, particularly in computational pathology, where domain-specialized FMs demonstrate strong generalization. However, real-world deployment is constrained by their poorly calibrated uncertainty awareness and degraded performance in low-data regimes requiring few-shot adaptation strategies, leading to unreliable and inefficient diagnostic workflows. Conformal Prediction (CP) is an uncertainty quantification framework that offers distribution‑free, finite‑sample coverage guarantees for ensuring safer deployment in such settings. In this work, we explore the integration of various CP methods with pathology foundation models using three few-shot adaption strategies for classification tasks across two datasets. To assess the clinical effectiveness of these approaches, we propose four novel metrics aimed at improving clinical reliability and alleviating diagnostic workload in few-shot settings. Our results demonstrate that Conformal Prediction methods enhance the reliability of pathology foundation models and offer actionable uncertainty estimates to enable safe and efficient deployment in few-shot pathological classification workflows, with the LAC method achieving the best overall performance.

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