Singapore

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.

AAAI 2026

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

operational impact

queueing simulation

radiology workflow

calibrated risk estimation

ai triage

deployable ai

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 purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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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.

technical paper

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.

Towards Reliable Few-Shot Adaptation of Pathology Foundation Models via Conformal Prediction

Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. Medical Visual Question Answering (MedVQA) has particularly benefited from these developments. However, despite Bangla being one of the most widely spoken languages globally, there exists no established MedVQA benchmark for it. To address this gap, we introduce BanglaMedVQA, a dataset comprising clinically validated image–question–answer pairs, along with a comprehensive evaluation of current foundation models on this resource. Consistent with prior findings that report low performance of current models on English MedVQA benchmarks, our analysis reveals that Bangla performance is substantially lower, reflecting the challenges inherent to low-resource languages. Even top-performing models such as Gemini and GPT-4.1 mini fail to accurately answer specialized diagnostic questions, indicating severe limitations in fine-grained medical reasoning. Although certain open-source models, such as Gemma-3, occasionally outperform these models in general categories, they too struggle with clinically complex questions underscoring the urgent need for top-notch evaluation method.

How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking

We study an ultrasound-first, radiation-preserving policy for developmental dysplasia of the hip (DDH) that requests a radiograph only when needed. We (i) pretrain modality-specific encoders (ResNet‑18) with SimSiam on a large unlabelled registry (37{,}186 ultrasound; 19{,}546 radiographs), (ii) freeze the backbones and fit small, measurement-faithful heads on DDH relevant landmarks and measurements (iii) calibrate a one-sided conformal deferral rule on ultrasound predictions that guarantees coverage on a held-out calibration set. Ultrasound heads predict Graf and femoral head coverage; X-ray heads predict acetabular index (AI), center–edge (CE) angle, and IHDI grade . On our held out labeled evaluation set, ultrasound measurement error is modest (e.g., MAE 9.7, coverage MAE 14.0%), while radiographic probes achieve AI and CE MAEs of 7.6 and 8.9, respectively. The calibrated US-only policy is explored across rule families (alpha-only; alpha OR coverage; alpha AND coverage), uncertainty inflation factors, and per-utility trade-offs using decision-curve analysis. Conservative settings yield high coverage (e.g., 0.90 for ) with near-zero US-only rates; permissive settings (e.g., alpha OR coverage at larger deltas) achieve non-zero US-only throughput with expected coverage trade-offs. The result is a simple, reproducible pipeline that turns limited labels into interpretable measurements and tunable selective imaging curves suitable for clinical handoff and future external validation.

Radiation-Preserving Selective Imaging for Pediatric Hip Dysplasia: A Cross-Modal Ultrasound–Xray Policy with Limited Labels

Malaria remains a major health challenge in Africa, with accurate species identification critical for prompt treatment and control, especially in resource-limited settings like Rwanda. This study systematically benchmarks state-of-the-art detection architectures for automated multi-species malaria parasite detection from high-resolution Giemsa-stained blood smear images, addressing the persistent problems of class imbalance and the difficulty of detecting small Plasmodium falciparum ring forms. We evaluate and optimize YOLO-SPAM, YOLO-Para, and YOLOv12 models, applying a novel species-specific augmentation protocol with copy-paste and noise injection. YOLOv12, trained with these protocols, achieves outstanding overall performance ( , and demonstrates significant improvement in detecting small, clinically relevant P. falciparum parasites over non-attention YOLO methods). Comparative experiments reveal that targeted data augmentation and strategic model selection can overcome significant class imbalance, achieving reliable multi-species differentiation and precise localization even for morphologically subtle classes. Our findings validate the practical promise of advanced object detection models, such as YOLOv12, for malaria diagnosis workflows and support their future deployment in real-world microscopy labs in Rwanda and similar endemic regions.

Bridging the Gap in Malaria Diagnostics: An Attention-Centric YOLO Framework with Species-Specific Augmentation for Tiny Parasite Detection in Low-Resource Settings

Antimicrobial resistance (AMR) is becoming a bigger threat to global health, especially in low and middle-income countries (LMICs) where antibiotics are often given out without a prescription. This study presents a hybrid digital Artificial Intelligence(AI) framework aimed at improving rational antibiotic utilization by integrating pharmacies, telemedicine practitioners, and policymakers via an integrated digital infrastructure. The digital intervention system was implemented in 28 community pharmacies and showed significant improvements in how people used antibiotics. For example, the percentage of people who self-medicated with antibiotics dropped from 4.05% to 0.86%, and the percentage of people who bought antibiotics with a prescription jumped from 27.6% to 43.5%. We propose an Artificial Intelligence(AI) framework that combines a machine learning (ML)-based and a large language model (LLM)-based symptom checker for intelligent triage and clinical decision support, based on this foundation. These models will allow for efficient analysis of both structured and narrative symptom data, which will help pharmacists give advice and doctors refer patients right away. The suggested model demonstrates a scalable, data-driven, and human-in-the-loop approach to antibiotic stewardship, with the potential to significantly enhance AMR mitigation and healthcare accessibility in LMIC settings.

Integrating AI-Driven Triage into Digital Pharmacy Systems for Rational Antibiotic Use in Low-Resource Settings.

Explainability, domain generalization, and rare-class reliability are critical challenges in medical AI, where deep models often fail under real-world distribution shifts and exhibit bias against infrequent clinical conditions. This paper introduces XAI-MeD, an explainable medical AI framework that integrates clinically accurate expert knowledge into deep learning through a unified neuro-symbolic architecture. XAI-MeD is designed to improve robustness under distribution shift, enhance rare-class sensitivity, and deliver transparent, clinically aligned interpretations. The framework encodes clinical expertise as logical connectives over atomic medical propositions, transforming them into machine-checkable, class-specific rules. Their diagnostic utility is quantified through weighted feature satisfaction scores, enabling a symbolic reasoning branch that complements neural predictions. A confidence-weighted fusion integrates symbolic and deep outputs, while a Hunt-inspired adaptive routing mechanism— guided by Entropy Imbalance Gain (EIG) and Rare-Class Gini mitigates class imbalance, high intra-class variability, and uncertainty. We evaluate XAI-MeD across diverse modalities, on four challenging tasks: (i) Seizure Onset Zone (SOZ) localization from rs-fMRI, (ii) Diabetic Retinopathy grading, (iii) cardiac function assessment from echocardiograms, and (iv) ECG-based cardiac analysis. Experiments across ten multicenter datasets demonstrate substantial performance improvements, including 6% gains in cross-domain generalization and a rare-class F1 score of 0.916, far outperforming state-of-the-art deep learning baselines (F1 = 0.63). Ablation studies confirm that the clinically grounded symbolic components act as effective regularizers, ensuring robustness to distribution shifts. XAI-MeD thus provides a principled, clinically faithful, and interpretable approach to multimodal medical AI.

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

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