Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.

Knee osteoarthritis (KOA) is a prevalent condition globally, leading to significant pain and disability, particularly in individuals over the age of 40. While exercise has been shown to reduce symptoms and improve physical function and quality of life in patients with KOA, long-term adherence to exercise programs remains a challenge due to the lack of ongoing support. To address this, a chatbot was developed using large language models (LLMs) to provide evidence-based guidance and promote adherence to treatment. A systematic review conducted under the PRISMA framework identified relevant clinical guidelines that served as the foundational knowledge base for the chatbot. The Mistral 7B model, optimized with Parameter-Efficient Fine-Tuning (PEFT) and Mixture-of-Experts (MoE) techniques, was integrated to ensure computational efficiency and mitigate hallucinations, a critical concern in medical applications. Additionally, the chatbot employs Self-Reflective Retrieval-Augmented Generation (SELF-RAG) combined with Chain of Thought (CoT) reasoning, enabling dynamic query reformulation and the generation of accurate, evidence-based responses tailored to patient needs. The chatbot was evaluated by comparing pre- and post-improvement versions and against a reference model (ChatGPT), using metrics of accuracy, relevance, and consistency. The results demonstrated significant improvements in response quality and conversational coherence, emphasizing the potential of integrating advanced LLMs with retrieval and reasoning methods to address critical challenges in healthcare. This approach not only enhances treatment adherence but also strengthens patient–provider interactions in managing chronic conditions like KOA.

Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, an essential tool to support pathologists. Deep learning models like Convolutional Neural Networks and Vision Transformers improve the extraction of these feature descriptors. These models typically generate embeddings by leveraging deeper single-scale linear layers or advanced pooling layers. However, these embeddings, by focusing on local spatial details at a single scale, miss out on the richer spatial context from earlier layers. This gap suggests the development of methods that incorporate multi-scale information to enhance the depth and utility of feature descriptors in histopathological image analysis. In this work, we propose the Local–Global Feature Fusion Embedding Model. This proposal is composed of three elements: (1) a pre-trained backbone for feature extraction from multi-scales, (2) a neck branch for local–global feature fusion, and (3) a Generalized Mean (GeM)-based pooling head for feature descriptors. Based on our experiments, the model’s neck and head were trained on ImageNet-1k and PanNuke datasets employing the Sub-center ArcFace loss and compared with the state-of-the-art Kimia Path24C dataset for histopathological image retrieval, achieving a Recall@1 of 99.40% for test patches.

Addressing the astronomical challenges posed by the interplay of data volume, AI sophistication, and energy consumption is crucial for the future of astronomy. As astronomical surveys continue to produce vast amounts of data, the computational and energy demands for galaxy classification have escalated, necessitating more efficient and sustainable approaches. This study presents a novel application of tensor factorization within the Faster R-CNN framework, resulting in the development of our model, T-Faster R-CNN, designed to enhance both the energy efficiency and computational performance of deep learning models used in galaxy classification. By integrating tensor factorization, our T-Faster R-CNN significantly reduces the model’s complexity, memory footprint, and CO₂ emissions, while maintaining, and in some cases even improving, the accuracy of morphological classification. The effectiveness of this optimized model is validated using data from the Galaxy Zoo DECaLS, where it demonstrates substantial improvements in computational efficiency without compromising classification precision. Furthermore, this research incorporates green code principles, emphasizing reductions in energy consumption and environmental impact in computational astronomy. The T-Faster R-CNN model offers a resource-efficient, sustainable methodology for analyzing large-scale astronomical data, addressing the critical need for greener computational practices in the era of big data.

Introduction Musculoskeletal disorders (MSDs) represent a significant global health burden, leading to substantial disability and socioeconomic impact. With the rise of artificial intelligence (AI), particularly large language models-driven conversational agents (chatbots), there is potential to enhance the management of MSDs. However, the application of AI-powered chatbots in this population has not been comprehensively synthesized. Objective To explore the current and potential use of AI-powered chatbots in the management of MSDs. The review will map out the targeted diseases, the purposes of chatbot interventions, the clinical tools or frameworks utilized in training these systems, and the evaluated outcomes in clinical settings. Methods This scoping review will follow the PRISMA-ScR guidelines, with a comprehensive search across multiple databases including Medline (Ovid MEDLINE), Embase (Ovid), ISI Web of Science (wos; clarivate) and ClinicalTrials.gov. Studies involving adults with MSDs, regardless of publication status, language, or year, will be included. The scoping review will exclude studies using non-AI chatbots or human health coaches. Data extraction and synthesis will focus on demographic characteristics, chatbot methods, outcomes, and thematic analysis. Ethics and dissemination Formal ethical approval is not required as this study involves neither human participants nor unpublished secondary data. Findings of the scoping review will be disseminated through professional networks, conference presentations and publication in a scientific journal. Strengths and limitations of this study We developed a search strategy with high sensitivity to retrieve studies This scoping review will not exclude studies by publication status, language, or year of publication.