Public Health & Policy

Ethics

Latest AI and machine learning research in ethics for healthcare professionals.

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Coronary p-Graph: Automatic classification and localization of coronary artery stenosis from Cardiac CTA using DSA-based annotations.

Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implicati...

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial.

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS),...

AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows.

This study evaluates the effectiveness of artificial intelligence (AI) tools (ChatGPT-4, Claude, and...

The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.

This paper discusses the responsible use of artificial intelligence (AI) in public health and in med...

Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.

Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the sk...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Obesity poses a significant health threat, contributing to the development of noncommunicable diseas...

Machine learning-based non-invasive continuous dynamic monitoring of human core temperature with wearable dual temperature sensors.

Due to the growing demand for personal health monitoring in extreme environments, continuous monitor...

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial u...

Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes.

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of grea...

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thr...

Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.

BACKGROUND: The integration of big data and artificial intelligence (AI) in healthcare, particularly...

Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms.

Microorganisms are one of the most promising biomarkers for cancer, and the relationship between mic...

Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The growing adoption of Large Language Models (LLMs) in medicine has raised important qu...

Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications.

Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemi...

Application of an AI-Based Model for Non-Invasive Sonographic Assessment for Injection Laryngoplasty.

OBJECTIVE: Hyaluronic acid (HA) can be degraded over time. However, the persistence of the effects a...

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning.

Occupational lung diseases, such as silicosis, are a significant global health concern, especially w...

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