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Open challenges and opportunities in federated foundation models towards biomedical healthcare.
BioData Min
January 2025
School of Computer Science, Fudan University, Shanghai, China.
This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions.
View Article and Find Full Text PDFRaman spectroscopy integrated with machine learning techniques to improve industrial sorting of Waste Electric and Electronic Equipment (WEEE) plastics.
J Environ Manage
January 2025
GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico, Edificio 202, 48170, Zamudio, Spain.
Current industrial separation and sorting technologies struggle to efficiently identify and classify a large part of Waste of Electric and Electronic Equipment (WEEE) plastics due to their high content of certain additives. In this study, Raman spectroscopy in combination with machine learning methods was assessed to develop classification models that could improve the identification and separation of Polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS), Polycarbonate (PC) and the blend PC/ABS contained in WEEE streams, including black plastics, to increase their recycling rate, and to enhance plastics circularity. Raman spectral analysis was carried out with two lasers of different excitation wavelengths (785 nm and 1064 nm) and varying setting parameters (laser power, integration time, focus distance) with the aim at reducing the fluorescence.
View Article and Find Full Text PDFVisual short-term memory binding and attentional processes during object-scene integration are preserved in mild cognitive impairment.
Cortex
December 2024
Department of Psychology, Sapienza University of Rome, Rome, Italy; IRCCS Fondazione Santa Lucia, Rome, Italy. Electronic address:
Binding, a critical cognitive process likely mediated by attention, is essential for creating coherent object representations within a scene. This process is vulnerable in individuals with dementia, who exhibit deficits in visual working memory (VWM) binding, primarily tested using abstract arrays of standalone objects. To explore how binding operates in more realistic settings across the lifespan, we examined the impact of object saliency and semantic consistency on VWM binding and the role of overt attention.
View Article and Find Full Text PDFUnderstanding pathways for effective interprofessional education: a thematic analysis of medical and nursing students' insights.
BMC Med Educ
December 2024
School of Medicine, University of Debrecen, Debrecen, Hungary.
Background: As the healthcare field is undergoing a paradigm shift, the role of interprofessional education (IPE) is being widely accepted and recognized. Despite this, IPE interventions are not employed globally and the use of such interventions is variable. Additionally, there is an ongoing debate over how and when to employ IPE interventions.
View Article and Find Full Text PDFImplementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.
Eur Radiol
December 2024
Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.
Objectives: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by radiologists.
Materials And Methods: An algorithm was deployed in the emergency room at a single tertiary care hospital for the detection of LVO on CT angiography (CTA) between September 1st-27th, 2021. A retrospective analysis of the algorithm's accuracy was performed.
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