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Accuracy of Current Large Language Models and The Retrieval Augmented Generation Model in Determining Dietary Principles in Chronic Kidney Disease.
J Ren Nutr
January 2025
Assistant professor, Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Burdur Mehmet Akif Ersoy University, Burdur, Türkiye.
Objective: Large Language Models (LLMs) have emerged as powerful tools with significant potential for quickly accessing information in the nutrition and health, as in many fields. Retrieval augmented generation (RAG) has been included among artificial intelligence (AI) powered chatbot structures as a framework developed to increase the accuracy and ability of LLMs. This study aimed to evaluate the accuracy of LLMs (GPT4, Gemini, and Llama) and RAG in determining dietary principles in chronic kidney disease.
View Article and Find Full Text PDFLeveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders.
Sci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
View Article and Find Full Text PDFARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.
J Biomed Inform
January 2025
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.
View Article and Find Full Text PDFPic2Plate: A Vision-Language and Retrieval-Augmented Framework for Personalized Recipe Recommendations.
Sensors (Basel)
January 2025
Department of Computer Engineering, Dongseo University, Busan 47011, Republic of Korea.
Choosing nutritious foods is essential for daily health, but finding recipes that match available ingredients and dietary preferences can be challenging. Traditional recommendation methods often lack personalization and accurate ingredient recognition. Personalized systems address this by integrating user preferences, dietary needs, and ingredient availability.
View Article and Find Full Text PDFAI-Powered Neurogenetics: Supporting Patient's Evaluation with Chatbot.
Genes (Basel)
December 2024
Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.
Background/objectives: Artificial intelligence and large language models like ChatGPT and Google's Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini's potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders.
Methods: By analyzing the model's performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments.
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