Latest AI and machine learning research in product alert for healthcare professionals.
Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of PTE remains challenging due to heterogeneous clinical data, limited positive cases, and reliance on resource-intensive neuroimaging data. We investigate whether routinely collected acute clinical records alone can support early PTE prediction using ...
Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators. Here, we evaluate an LLM jury composed of three frontier AI models scoring 3333 diagnoses on 300 real-world middle-income country (MIC) hospital cases. Model performance was benchmarked against expert clinician panel and independent human r...
Recent EEG studies of human quiet stance have identified beta-band event-related desynchronization (beta-ERD) and synchronization (beta-ERS; post-move...
The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL)...
Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explain...
ABSTRACT Background: RNA editing is a post-transcriptional modification that alters the sequence of an RNA transcript. Two types of RNA editing were f...
Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality...
We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which...
Accurate delineation of the Clinical Target Volume (CTV) is essential for radiotherapy planning, yet remains time-consuming and difficult to assess, e...
Generative object compositing methods have shown remarkable ability to seamlessly insert objects into scenes. However, when applied to real-world cata...
Organisations with limited data and computational resources increasingly outsource model training to Machine Learning as a Service (MLaaS) providers, ...
Foundation models have achieved remarkable results in medical image analysis. However, its large network architecture and high computational complexit...
Ask a frontier model how to taper six milligrams of alprazolam (psychiatrist retired, ten days of pills left, abrupt cessation causes seizures) and it...
Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) conte...
Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-to-...
Breath acetone represents a promising non-invasive biomarker for monitoring fat oxidation during exercise. However, its utility is limited by confound...
Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of s...
As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their p...
An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenario...
Background: The integration of artificial intelligence (AI) into clinical practice holds transformative potential for healthcare in West Africa, but s...