Background/Objectives: The objective evaluation of regional dietary intake remains a core challenge in personalized health management due to complex plate presentations and a lack of culturally specific dataset benchmarks. Methods: This study introduces a confidence-aware hybrid vision–language framework engineered for traditional Turkish food recognition and structured nutritional assessment. Results: We curate a balanced dataset containing 14,711 verified images spanning 40 representative Turkish culinary classes to train and evaluate seven deep learning architectures. Among the visual models, EfficientNet V2-L achieved the highest standalone performance with an accuracy of 93.47%, 0.92 macro-precision, 0.92 macro-recall, and a 0.92 F1 score. To overcome visual ambiguity and automate content analysis, a confidence-aware routing strategy escalates uncertain predictions (τ<0.70) or user-rejected classifications to the Google Gemini 2.5 Flash multimodal large language model (MLLM). Conclusions: This hybrid paradigm yields a combined classification accuracy of 95.50% while validating portion weight estimations within a mean absolute error (MAE) of 18.42 g and total energy within 36.75 kcal. Fully realized as a cross-platform Flutter mobile application, the end-to-end pipeline demonstrates localized plate detection, adaptive portion analysis, and structured nutrient tracking, providing a scalable design for consumer-facing digital nutrition platforms.
Furkan Göz, Muhammad Jamil, A. Kavak et al.· Nutrients· 0 citations
Diabetes management requires continuous, personalized guidance for medication use, nutrition, glucose monitoring, and daily lifestyle decisions. However, many digital health tools cannot provide comprehensive support tailored to each patient’s medical condition and changing needs. Rule-based conversational systems can be safe but are often rigid and limited in natural interaction, whereas standalone large language models (LLMs) can produce fluent responses but may generate unsupported or clinically unsafe information, lack clear source attribution, and cannot reliably use patient-specific information or updated medical knowledge. Other challenges include limited personalization and fragmented diabetes knowledge distributed across clinical guidelines, patient records, nutritional databases, glucose-monitoring data, and medication safety resources. To address these limitations, this paper presents a Retrieval-Augmented Large Language Model (RA-LLM) architecture for trustworthy conversational AI in diabetes care which is a part of our AI-based Diabetes Care (AIDCare) mHealth solution. The proposed framework integrates clinical guidelines, structured electronic health record attributes, nutrition knowledge, optional glucose-monitoring context, and pharmaceutical safety rules. It combines clinical entity recognition, hybrid dense–sparse retrieval, patient-context filtering, cross-encoder reranking, evidence-constrained prompt construction, and multi-layer safety verification to generate personalized, evidence-supported conversational responses. The proposed system was evaluated on a controlled benchmark of 10,000 synthetic diabetes interaction scenarios covering medication guidance, nutrition, glucose monitoring, complication awareness, and lifestyle management. Compared with a vanilla LLM baseline, the RA-LLM improved evidence-grounded response correctness from 78.3% to 94.7%, increased source attribution reliability from 34.0% to 94.0%, and reduced the predefined safety-violation rate by approximately 89%. These findings show the potential of multi-source retrieval, patient-context integration, and safety-aware conversational generation for improving AI-assisted diabetes self-management. As the evaluation is based on synthetic scenarios, real-world clinical validation remains necessary.
Muhammad Jamil, A. Kavak, Sema Bayraktar et al.· IEEE Access· 0 citations
