Latest AI and machine learning research in work force for healthcare professionals.
Background: A critical radiologist shortage exists in India, leading to delayed chest radiograph (CXR) interpretation. This leads to disease progression, higher morbidity, and mortality. Artificial intelligence-based CXR interpretation by Lenek Intelligent Radiology Assistant (LIRA) is a promising solution. This study aims to establish the screening and triaging capabilities of LIRA by assessing i...
Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic features. Existing JEAs rely on implicit or explicit batch interaction -- via negative sampling or statistical regularization -- to prevent representation collapse. This reliance becomes problematic in regimes where batch size...
The diversity of training datasets is usually perceived as an important aspect to obtain a robust model. However, the definition of diversity is often...
Explainable Artificial Intelligence (XAI) is increasingly essential as AI systems are deployed in critical fields such as healthcare and finance, offe...
Background and Objective: Increasing screening volumes, combined with global shortage of radiologists and a high proportion of normal mammograms, chal...
Video quality significantly affects video classification. We found this problem when we classified Mild Cognitive Impairment well from clear videos, b...
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens. While prior work shows th...
Malawi's HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 mo...
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens. While prior work shows th...
Surgical scene Multi-Task Federated Learning (MTFL) is essential for robot-assisted minimally invasive surgery (RAS) but remains underexplored in surg...
Utility companies increasingly rely on drone imagery for post-event and routine inspection, but training accurate defect-type classifiers remains diff...
In order to navigate complex traffic environments, self-driving vehicles must recognize many semantic classes pertaining to vulnerable road users or t...
Understanding natural selection can help shed light on the genetics underpinning adaptive evolution. The widespread availability of large-scale human ...
Background: The 2024 blood culture bottle shortage brought diagnostic resource allocation to the forefront, reflecting persistent, foundational challe...
Equivocal 3D lesion segmentation exhibits high inter-observer variability. Conventional deterministic models ignore this aleatoric uncertainty, produc...
In previous work, we achieved state-of-the-art performance on ChestX-ray14 (ROC-AUC 0.940, F1 0.821) using pretraining diversity and clinical metric o...
While protein language models (PLMs) have shown great promise for protein design, their performance is fundamentally constrained by the diversity and ...
Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to high...
Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive...
Background: Systematic reviews are important for informing public health policies and program selection; however, they are time- and resource-intensiv...