COVID-19 diagnosis using AI

Latest papers

• Geometry-Aware Adaptation for Pretrained Models

  Nicholas Roberts, Xintong Li, Dyah Adila, Sonia Cromp, Tzu-Heng Huang, Jitian Zhao, Frederic Sala · 2 October 2026

  Machine learning models -- including prominent zero-shot models -- are often trained on datasets whose labels are only a small proportion of a larger label space. Such spaces are commonly equipped with a metric that relates the labels via distances between them. We propose a simple approach to explo…

• PLRS-IC: A Dual-Calibration Framework for Chest X-Ray Vision-Language Alignment

  Qixing Zhao, Jinpeng Li · 1 October 2026

  Fine-grained vision-language alignment in chest radiography enables zero-shot classification, grounding, and segmentation without task-specific annotations. However, this alignment is fundamentally hindered by two intertwined sources of ambiguity: projection-induced visual mismatch and patient-agnos…

• ChestPheNoT: Deployable, Auditable Label-Status-Evidence Extraction from Radiology Reports

  Kai Yu, Chenyu Zhu, Zaifu Zhan, Meijia Song, Min Zeng, Xiaoyi Chen, Mingquan Lin, Rui Zhang · 29 September 2026

  Structured phenotype extraction from radiology reports supports cohort construction, quality auditing, and clinical analytics, but practical deployment requires local inference and auditable predictions, while expert annotations remain scarce. Conventional labelers provide structured findings and as…

• CRC-Router: Risk-Constrained Routing for Medical Agentic AI Systems

  Xueyang Li, Mingze Jiang, Gelei Xu, Jun Xia, Ching-Hao Chiu, Mengzhao Jia, Danny Z. Chen, Yiyu Shi · 28 September 2026

  Agentic AI systems are increasingly being explored in medical imaging to improve throughput and reduce clinician workload; however, safe deployment remains challenging because autonomous errors may propagate into downstream clinical decisions. A central requirement is therefore not only strong predi…

• Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation

  Janhavi Prabhu, Sahil, Akshay V, Shivam Shukla, Manoj Tadepalli, Preetham Putha · 25 September 2026

  Long-tailed chest X-ray classification requires visual representations that capture both common abnormalities and subtle, infrequent findings. We propose Med-AR-8B and Med-AR-2B, two radiology-native autoregressive vision-language models pretrained with structured reports, abnormality-focused text, …

• Clinical Knowledge Graphs for Chest X-Ray Device Reasoning

  Harshil Lodhiya · 25 September 2026

  Chest radiographs are routinely used to verify the position of catheters, tubes, and other support devices. Existing image models often return labels or segmentations, while report-processing systems structure text without access to image geometry. We present an uncertainty-aware clinical knowledge …

• NV-Reason-CT: 3D Visual Language Model for CT Analysis

  Andriy Myronenko, Dong Yang, Yucheng Tang, Baris Turkbey, Benjamin Simon, Stephanie Harmon, Rikhil Makwana, Mariam Aboian, Sena Azamat, Ibrahim Ethem Hamamci, Sezgin Er, Bjoern Menze, Marc Edgar, Yufan He, Pengfei Guo, Daguang Xu · 24 September 2026

  We present NV-Reason-CT, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning. The model couples a native 3D vision transformer with a language model, passing all visual tokens and their explicit 3D coordinates into lang…

• Learning to Defer with Guidance on Real World Medical Data

  Emma Sun, Joshua Strong, Alison Noble · 23 September 2026

  Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries potential safety concerns and low specificity may in practice lead to increased clinician workload. Learning to Defer (L2D) addresses this by selective…

• Anatomy-Decomposed Chest Computed Tomography (CT) Projections as Scalable Supervision for Bone Suppression in Chest Radiographs

  Mrunmay Angaitkar, Piyush Kumar, Aarjav Satia, Pranav Rao, Ashish Mittal, Manoj Tadepalli, Preetham Putha · 22 September 2026

  Bone overlap can obscure abnormalities in chest radiographs, while scarce paired training data limit supervised bone suppression. We address this challenge with a digitally reconstructed radiograph (DRR) framework that converts chest computed tomography (CT) into paired supervision for component sup…

• Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models

  Xiaodan Xing, Rajat R. Rasal, Julia A. Meister, Sara Ghorayeb, Galvin Khara, Jessica Schrouff · 22 September 2026

  Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods have improved perceptual quality, identity preservation and faithfulness to an underlying causal model, but their adoption in healthcare is limited by s…

• Representation-guided in-context learning for medical image interpretation with multimodal large language models

  Minda Zhao, Fangyu Hu, Yan Luo, Yutong Yang, Jiahui Cai, Kaichen Zhou, Manling Li, Paul Liang, Yilun Du, Lucy Q. Shen, Mengyu Wang · 22 September 2026

  Medical image interpretation is central to diagnosis and care, yet adapting general-purpose multimodal large language models (MLLMs) often requires resource-intensive domain-specific fine-tuning. Here we introduce representation-guided in-context learning (RG-ICL), a training-free inference framewor…

• Beyond Benchmark Scores: Auditing Medical Vision-Language Models for Chest X-Ray Tuberculosis Screening

  Mushir Akhtar, M. Tanveer, Mohd. Arshad · 21 September 2026

  A medical model's benchmark score does not establish that the same conclusion holds under a different evaluation. This study tests whether claims about model ranking, score reliability and screening performance survive changes in cohort, prompt, negative spectrum, specified prevalence and operating …

• Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification

  Ying-Chih Lin, Po-Chih Kuo, Yong-Sheng Chen · 21 September 2026

  Multi-label few-shot learning (MLFSL) remains a significant challenge in medical image analysis (MIA). Current metric-based meta-learning methods face two critical limitations in MIA. First, conventional prototype generation often entangles irrelevant disease information, leading to contaminated pro…

• Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging

  Johannes Kaiser, Florian Braunmiller, Daniel R\"uckert, Georgios Kaissis · 17 September 2026

  AI models for multimodal medical imaging must balance modality-specific specialization with cross-modal shared representations, a trade-off that pure Mixture-of-Experts (MoE) architectures currently fail to satisfy. Expert-based routing improves in-domain learning but may sacrifice cross-modal signa…

• ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs

  Sebasti\'an Andr\'es Cajas Ord\'o\~nez, Maximin Lange, Quang Bui, Anqi Peter Li, Felipe Ocampo Osorio, Rafi Al Attrach, Kushul Reddy Palakala, Sahil Kapadia, Zakaria Laouabdia Sellami, Xinyue Zhang, Ashley Zhang, Leo Anthony Celi · 16 September 2026

  A radiology report can already answer a clinical question, so it is hard to tell whether a vision-language model also uses the image. ModaLens, a paired image-swap audit, measures how report availability changes image sensitivity: MedGemma-27B on 3,199 paired MIMIC-CXR cases from 293 patients, all 1…

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation

  Xinyue Xu, Hongbin Lin, Juangui Xu, Hualiang Wang, Lehan Wang, Lijie Hu, Weiyang Liu, Adrian Weller, Xiaomeng Li · 16 September 2026

  Medical imaging modalities such as ultrasound and X-ray are widely used in clinical practice, where diagnosis follows a structured, evidence-driven workflow aligned with standardized criteria. While multimodal large language models (MLLMs) show promise for automated medical report generation, most e…

• Parallel Training Using a CNN-DNN Architecture for Accelerated Development of Diagnostic Models

  Janine Weber-Hamacher, Astha Jaiswal, Philipp Fervers, Dorotya M\'or\'e, Athanasios Giannakis, Ricarda Fischbach, Andreas Michael Bucher, Rahil Shahzad, Jonathan Kottlors, Thorsten Persigehl, Axel Klawonn · 14 September 2026

  Artificial intelligence has shown promise in assisting radiologists in imaging-based diagnosis across a wide range of diseases. Efficient training of large deep learning models is essential to cope with extremely large data sets or dynamically growing disease data, like in a pandemic like situation.…

• Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection

  Naveenraj Kamalakannan, Sri Ram Macharla, M Kanimozhi, M S Sudhakar · 11 September 2026

  The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and…

• Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models

  Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan, Alison Q. O'Neil, Sotirios A. Tsaftaris · 10 September 2026

  Latent Diffusion Models have shown remarkable results in text-guided image synthesis in recent years. In the domain of natural (RGB) images, recent works have shown that such models can be adapted to various vision-language downstream tasks with little to no supervision involved. On the contrary, te…

• Integrating Unimodal and Vision-Language Representations in Latent Space for Multi-Label Chest X-Ray Classification

  Quang-Huy Tran, Duc-Tuan Ngo, Minh-Khoi Nguyen-Bui, Dang-Khoa Bui, Thanh-Trong Tran, Tuan-Khoi Nguyen, Hoang-Anh Ngo · 10 September 2026

  Multi-label chest X-ray classification has attracted considerable attention in recent years, with the effective use of visual representations and clinical semantic knowledge playing an important role. This study proposes a framework that combines unimodal representations from RAD-DINO with vision--l…

• OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

  Ayush Debnath, Ruelia Saha, Sudip Misra · 10 September 2026

  Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. T…

• Layer-Wise Gate-Controlled Prompt Truncation in a Multimodal Chest X-Ray Classifier

  Jingtao Lei, Hongji Li, Dexiang Shu · 9 September 2026

  Mixture of Prompt Experts (MoPE) adapts multimodal transformers through input-dependent prompt composition, while retaining a fixed prompt length. We investigate a layer-wise gating extension in a binary chest X-ray classification pilot study. The controller predicts a retention ratio for each sampl…

• A radiographic world model for clinical reasoning and evidence generation

  Suyang Xi, Songtao Hu, Shansong Wang, Mojtaba Safari, Luke del Balzo, Ehsan Ul Karim, Mingzhe Hu, Kuo Zhang, Tonghe Wang, Ralph R. Weichselbaum, Xiaofeng Yang · 9 September 2026

  Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descriptions to generated images, although both arise from the same underlying radiographic state. A world-model formulation instead seeks to learn an inter…

• Cross-dataset transportability of pediatric chest X-ray deep learning across three countries: discrimination, calibration, operating-point failure, and limited-label recovery

  Nazim-E-Alam · 7 September 2026

  Background and Objective: External evaluation of medical-imaging AI is often collapsed into discrimination. We evaluated a computational protocol that separately tests discrimination, probability calibration, fixed operatingpoint transport, shortcut-associated signal, and limited-label recoverabilit…

• Cross-modal triage network: a multimodal deep learning framework for severity-based triage and visual explainability in chest radiographs

  Zinah Ghulam, Richa Mittal, Eranga Ukwatta · 7 September 2026

  Purpose: Increased number of chest radiograph (CXR) scans create a triage bottleneck, queueing urgent examinations behind routine ones. Existing AI tools are predominantly unimodal binary classifiers lacking severity awareness, and multimodal systems are rarely benchmarked against expert radiologist…