The study investigates why machine‑learning models for tuberculosis screening based on cough acoustics fail to generalize across datasets. Classical ML and deep‑learning classifiers achieved moderate performance within their own datasets (ROC‑AUC up to 0.755) but performed poorly on external data, often below 0.6. The authors found that audio features were more influenced by recording device and dataset than by TB status, and that device‑diverse training improved transfer while device mismatch degraded it. A clinical‑variable baseline showed more consistent generalization, suggesting acquisition‑specific variability is a stronger driver of poor generalizability than population shift.
By Wensi Zhang, Tomas Teijeiro, J\'er\^ome Thevenot, David Atienza
The study evaluates the robustness of medical vision‑language models for tuberculosis screening on chest X‑rays by testing them across multiple datasets, prompts, and evaluation settings. Three specialized models (BioMedCLIP, CheXficient, MedSigLIP) and a general OpenCLIP model were audited on 12,200 images, producing 244,000 model–image–prompt scores. Results show that no model consistently outperforms others across all cohorts and reliability criteria, with prompt changes and control group composition significantly affecting AUROC, and that high training‑set performance does not reliably transfer to external cohorts.
By Mushir Akhtar, M. Tanveer, Mohd. Arshad
arXiv:2608.29419v1 Announce Type: cross
Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over s...
By Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton
arXiv:2607. 04486v1 Announce Type: new Abstract: Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal.
By Ahmed M. Sayed (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Sondos A. Refaat (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Abdallah M. Mostafa (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Mariam S. El-Rahmany (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Ensaf Hussein Mohamed (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt, School of Information Technology and Computer Science)
EMFE (Efficient Mathematical Feature Extraction) is a lightweight, explainable machine‑learning framework that classifies single red‑blood‑cell images as parasitized or uninfected using five engineered features: Gray World color normalization, adaptive green‑channel thresholding, morphological spot detection, and classical classifiers. On the NIH LHNCBC malaria dataset (27,558 images from 200 patients), a tuned Random Forest achieved 94.6% pooled out‑of‑fold accuracy, 94.3% on a 40‑patient holdout, and outperformed deep‑learning baselines in an accuracy‑efficiency trade‑off. Ablation studies, synthetic perturbations, and explainability analyses identified spot saturation as the dominant discriminative feature and quantified the framework’s failure modes and patient‑level performance.
By Md Abdullah Al Kafi, Walayat Hussain, Mousumi Karmakar, Sumit Kumar Banshal, Ahmed Al Marouf
arXiv:2602. 03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress.
By Md Muhtasim Munif Fahim, M. Monimul Huq, M. Sabiruzzaman, Md Rezaul Karim
arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.
By Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh
arXiv:2607. 03466v1 Announce Type: cross Abstract: This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026.
By Joseph Itopa Abubakar, Jorge Jarme, Favour Igwezeke, Mary Adewunmi
arXiv:2609.12902v1 Announce Type: new
Abstract: Artificial intelligence has shown promise in assisting radiologists in imaging-based diagnosis across a wide range of diseases. Efficient training of l...
By 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
The paper introduces LLM4CKD, a framework that uses large language models (LLMs) for early chronic kidney disease (CKD) screening without task‑specific training. By employing clinically selected tabular features and structured prompt templates, the authors evaluate LLMs in zero‑shot and few‑shot settings against traditional machine learning, deep learning, and tabular foundation models. Results show that LLMs can match or outperform conventional methods in low‑data scenarios, though their performance varies with model choice and input complexity, highlighting a trade‑off between data efficiency and stability.
By Muhammad Ashad Kabir, Sirajam Munira
The article presents a comparative study of convolutional neural network (CNN) architectures for classifying COVID-19 from healthy lung images using CT and X‑ray scans. Multiple pre‑trained models—including VGG, DenseNet, ResNet, MobileNet, Xception, Inception, EfficientNet, and NasNet—were evaluated on two X‑ray and two CT datasets. ResNet and VGG achieved the highest accuracies, ranging from 95% to 98%, outperforming previous reports in the literature.
By Sarmad Khan, Basim Azam, Arslan Shaukat
The study evaluated ten chest X‑ray AI classification models on 12 datasets from six countries, focusing on image‑level classification and measuring specificity at 95% sensitivity. MAVL and MedKLIP achieved the highest specificity (≈0.37) but still produced many false positives, with performance dropping on institutional cohorts compared to public benchmarks. No single model excelled across all findings, countries, or lesion types, highlighting limited generalisability.
By Qinmei Xu, Yiheng Li, Wenya Zhao, Zihao Zhao, Chuanjun Xu, Ahmet Gorkem Er, Xianghao Zhan, Brittany Dashevsky, Mohammed Alawad, Marc von der St\"uck, Marvin Gazibari\'c, Daniel Truhn, Shunsuke Shibata, Ryosuke Horino, Mio Ito, Akio Hiwatashi, Suyu Li, Mengya Yang, Ya Liu, Zhiyuan Sun, Changsheng Zhou, Haruka Itakura, Xiao Li, Olivier Gevaert