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http://dx.doi.org/10.1200/JOP.19.00114 | DOI Listing |
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
IEEE Trans Biomed Eng
September 2025
Diagnostic ultrasound has long filled a crucial niche in medical imaging thanks to its portability, affordability, and favorable safety profile. Now, multi-view hardware and deep-learning-based image reconstruction algorithms promise to extend this niche to increasingly sophisticated applications, such as volume rendering and long-term organ monitoring. However, progress on these fronts is impeded by the complexities of ultrasound electronics and by the scarcity of high-fidelity radiofrequency data.
View Article and Find Full Text PDFPLOS Digit Health
September 2025
Singapore Health Services, Artificial Intelligence Office, Singapore.
Large Language Models (LLMs) show promise in augmenting digital health applications. However, development and scaling of large models face computational constraints, data security concerns and limitations of internet accessibility in some regions. We developed and tested Med-Pal, a medical domain-specific LLM-chatbot fine-tuned with a fine-grained, expert curated medication-enquiry dataset consisting of 1,100 question and answer pairs.
View Article and Find Full Text PDFStud Health Technol Inform
September 2025
MOLIT Institute, Heilbronn, Germany.
Introduction: In the context of precision oncology, patients often have complex conditions that require treatment based on specific and up-to-date knowledge of guidelines and research. This entails considerable effort when preparing such cases for molecular tumor boards (MTBs). Large language models (LLMs) could help to lower this burden if they could provide such information quickly and precisely on demand.
View Article and Find Full Text PDFJ Toxicol Sci
August 2025
Chemicals Assessment and Research Center, Chemicals Evaluation and Research, Institute, Japan (CERI).
Pharmaceutical manufacturing and storage processes pose the potential risk of chemicals migrating from the packaging materials into pharmaceuticals. These migrants, known as extractables and leachables (E&Ls), consist of various chemicals that may pose a risk to patients during therapeutic use. Although exposure to E&Ls via the intravenous route is of greater concern, there is almost no toxicity information for these chemicals to determine the Permitted Daily Exposure (PDE).
View Article and Find Full Text PDF