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.
arXiv:2608.21427v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the most common and dangerous bacterial ailments. Every year, it causes a large number of deaths worldwide. Although many d...
arXiv:2606. 15436v1 Announce Type: cross Abstract: Respiratory acoustic foundation models (FMs) excel at cough classification, yet their ability to predict continuous health quantities from cough audio remains largely unexplored, despite the clinical value of passive age, BMI, and disease probability estimation in settings where physical measurements are unavailable.
arXiv:2606. 02998v1 Announce Type: new Abstract: Automated cough analysis offers a path to low-cost respiratory screening, but most existing work stops at binary COVID-19 detection.
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.
Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely on pulmonary image content. This study evaluates...
arXiv:2608.21583v1 Announce Type: new Abstract: Oral cancer is a leading cause of mortality in low-to-middle-income countries, where a shortage of specialists delays diagnosis. While point-of-care sc...
arXiv:2606. 24510v1 Announce Type: new Abstract: Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise.
arXiv:2608.30467v1 Announce Type: new Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely...
arXiv:2607. 23883v1 Announce Type: cross Abstract: In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common diseases and away from rarer diseases.
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.
arXiv:2606. 08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy.
Shortcut learning occurs when classifiers rely on spurious correlations instead of true diagnostic features. The authors argue that this is essentially a calibration issue: unconstrained training aligns shortcut groups with their prevalence, causing over‑confidence in one group and under‑confidence in another. They introduce two encoder‑agnostic methods—a regularizer and a post‑hoc recalibration—to equalize prevalence across shortcut groups, achieving significant performance gains on chest‑drain‑pneumothorax benchmarks with both fine‑tuned CNNs and frozen foundation‑model backbones.