BACKGROUND: Racially minoritized populations in the United States (US), notably African American (AA) and American Indian/Alaska Native (AI/AN), experience disproportionately higher rates of chronic kidney disease (CKD), diabetes, and hypertension co...
BACKGROUND: Artificial intelligence (AI) remains poorly understood and its rapid growth raises concerns reminiscent of dystopian narratives. AI has shown the capability of producing new medical content and improving management through optimization an...
BMC medical informatics and decision making
Jul 15, 2025
Operationalizing resilience in public health systems is critical for enhancing adaptive capacity during crises. This study presents a Machine Learning (ML) -based approach to assess resilience of the health system. Using historical data from Brazilia...
BACKGROUND: Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitab...
Federated Learning (FL) enables artificial intelligence frameworks to train on private information without compromising privacy, which is especially useful in the medical and healthcare industries where the knowledge or data at hand is never enough. ...
This article challenges two dominant assumptions in the current ethical debate over the use of algorithmic Personalised Patient Preference Predictors (P4) in substitute judgement for incapacitated patients. First, I question the belief that the auton...
Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems. Various deep learning-based methods have been proposed for Lung cancer diagnosis. In this study, we proposed a Deep learning techniques-based integr...
The growing number of patients and the emergence of new symptoms and diseases make health monitoring and assessment increasingly complex for medical staff and hospitals. The execution of big and heterogeneous data gathered by medical sensors and the ...
With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy prese...
Healthcare is plagued with many problems that Artificial Intelligence (AI) can ameliorate or sometimes amplify. Regardless, AI is changing the way we reason towards solutions, especially at the frontier of public health applications where autonomous ...
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