Cyber-attacks on healthcare entities and leaks of personal identifiable information (PII) are a growing threat. However, it is now possible to learn sensitive characteristics of an individual without PII, by combining advances in artificial intelligence, analytics, and online repositories. We discuss privacy threats and privacy engineering solutions, emphasizing the selection of privacy enhancing technologies for various healthcare cases. Future solutions must consider dynamic flows of data throughout their lifecycle.
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http://dx.doi.org/10.1038/s41746-025-01520-6 | DOI Listing |
Radiol Artif Intell
March 2025
Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Durham, NC 27710.
Purpose To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights language models (LMs) and retrieval augmented generation (RAG) and to assess the effects of model configuration variables on extraction performance. Materials and Methods This retrospective study utilized two datasets: 7,294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2,154 pathology reports annotated for mutation status (January 2017 to July 2021). An automated pipeline was developed to benchmark the performance of various LMs and RAG configurations for structured data extraction accuracy from reports.
View Article and Find Full Text PDFFront Neurosci
February 2025
Department of Precision Machinery Engineering, College of Science and Technology, Nihon University, Funabashi, Chiba, Japan.
Easing the behavioral restrictions of those in need of care not only improves their own quality of life (QoL) but also reduces the burden on care workers and may help reduce the number of care workers in countries with declining birthrates. The brain-machine interface (BMI), in which appliances and machines are controlled only by brain activity, can be used in nursing care settings to alleviate behavioral restrictions and reduce stress for those in need of care. It is also expected to reduce the workload of care workers.
View Article and Find Full Text PDFBMC Psychiatry
March 2025
Department of Health Services Administration, China Medical University, Taichung, 406040, Taiwan.
Objective: The prevalence of mental illness in Taiwan increased. Identifying and mitigating risk factors for mental illness is essential. Inflammation may be a risk factor for mental illness; however, the predictive power of inflammation test values is unclear.
View Article and Find Full Text PDFStroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation.
View Article and Find Full Text PDFMedicine (Baltimore)
March 2025
Department of Biomedical and Laboratory Science, Africa University, Mutare, Zimbabwe.
The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clinical laboratory results, and imaging data, these technologies enhance diagnostic accuracy, enable robust prognostic modeling, and support personalized therapeutic interventions. Advanced ML algorithms, such as neural networks and ensemble learning, facilitate the discovery of novel biomarkers and refine risk stratification for hematological disorders, including leukemias, lymphomas, and coagulopathies.
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