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Care of terminally ill / Palliative care

Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.

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Image-based Explainable Artificial Intelligence Accurately Identifies Myelodysplastic Neoplasms Beyond Conventional Signs of Dysplasia

Evaluation of bone marrow morphology by experienced hematologists is key in the diagnosis of myeloid neoplasms, especially to detect subtle signs of dysplasia in myelodysplastic neoplasms (MDS). The majority of recently introduced deep learning (DL) models in cytomorphology rely heavily on manually drafted cell-level labels, a time-consuming, laborious process that is prone to substantial inter-ob...

AI-Driven Early Detection of Severe Influenza in Jiangsu, China: A Deep Learning Model Validated Through The Design of Multi-Center Clinical Trials and Prospective Real-World Deployment

Influenza causes about 650,000 deaths worldwide each year, and the high mortality rate of severe cases is closely related to subjective bias in clinical assessment and inconsistent diagnostic and treatment standards. To this end, this study developed and validated a deep learning-based model for early diagnosis of severe influenza that optimises risk stratification by integrating clinical data fro...

Automatic classification of eeg signals, based on image interpretation of spatio-temporal information

Brain-Computer Interface (BCI) applications provide a direct way to map human brain activity onto the control of external devices, without a need for ...

Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models

Extracting structured data from free-text medical records at scale is laborious, and traditional approaches struggle in complex clinical domains. We p...

Medication information extraction using local large language models

Medication information is crucial for clinical routine and research. However, a vast amount is stored in unstructured text, such as doctoral letters, ...

Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting

Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current dia...

Cardiac Magnetic Resonance Imaging in the German National Cohort: Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality Control

To develop a segmentation and quality control pipeline for short-axis cardiac magnetic resonance (CMR) cine images from the prospective, multi-center ...

Aneurysm Analysis Using Deep Learning

Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical ...

Exploring Healthcare Professionals’ Perspectives on Artificial Intelligence in Palliative Care: A Qualitative Study

The use of Artificial Intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routin...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Cardiac Function Assessment with Deep-Learning-Based Automatic Segmentation of Free-Running 4D Whole-Heart CMR

Free-running (FR) cardiac MRI enables free-breathing ECG-free fully dynamic 5D (3D spatial+cardiac+respiration dimensions) imaging but poses significa...

Toward the Autonomous AI Doctor: Quantitative Benchmarking of an Autonomous Agentic AI Versus Board-Certified Clinicians in a Real World Setting

Globally we face a projected shortage of 11 million healthcare practitioners by 2030, and administrative burden consumes 50% of clinical time. Artific...

Revolutionizing Lung Cancer Detection: Evaluating AI Models for VOC Analysis and Unveiling Key Exhaled Biomarkers

Volatile Organic Compounds (VOCs) are organic chemicals that readily vaporize at room temperature and are emitted from diverse sources, including pain...

Multimodal Speech and Text Models to Detect Suicidal Risks in Adolescents

Early detection of suicide risk in adolescents is crucial but faces challenges including stigma, reluctance to disclose suicidal thoughts, and limited...

Wireless Colorimetric Multi-Biomarker Sensing to Enable Critical Neonatal Monitoring

Clinical monitoring in the most vulnerable patients such as newborns relies on invasive and costly procedures and/or wired sensor surveillance, increa...

AI-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning with the Radiation Planning Assistant (RPA)

Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps and clinical hand-offs that contribute to treat...

Lower pre-treatment TMS-evoked cortical reactivity and alpha-band oscillatory dynamics predict efficacy of primary motor cortex neuromodulation for chronic pain

Repetitive transcranial magnetic stimulation (rTMS) targeting the primary motor cortex (M1) provides significant pain relief in approximately 45% of p...

Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis

There is a lack of automated pipelines for diagnostic classification of point-of-care tests for neglected tropical diseases. Here we present an end-to...

Cardiac Measurement Calculation on Point-of-Care Ultrasonography with Artificial Intelligence

Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited setti...

Deep learning on 3D ECG geometry predicts ischemia

Three-dimensional (3D) electrocardiography (ECG) is a recent methodological advance that extends the dimensionality of the standard ECG, enabling geom...

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