2026· European Journal of Computer Sciences and Informatics· Vol 3, pp. 181· 0 citations
TL;DR
The secure authentication and regulatory compliance make HealthAssist a scalable and implementable system for real-world clinical deployment, with demonstrated clinical acceptability across expert panel evaluation.
Abstract
Aim/Background: The HealthAssist architecture is a complex MLLM which seeks to provide a dependable and reliable healthcare assistance through the use of artificial intelligence, particularly in underserved areas. It uses the Mistral Large 3 MoE model of 675 billion parameters while using only 41 billion at any point during execution. The healthassist system has an independent 2.5 billion parameter vision encoder used in the interpretation of medical imagery, alongside a large context of 256,000 tokens. Methods: This system incorporates seven different clinical services that include: medication consultation, disease identification, lab results interpretation, prescription decryption, symptom-based diagnosis, emergency services, and recommending hospitals locally. The architecture is made to ensure security through the use of SHA-256 authentication and compliance with the FDA SaMD and EU AI Act framework. Results: The results showed that the system attained an overall accuracy of 90.54% (CI=89.21-91.87), 94.64% semantic similarity, 86.54% METEOR, 88.12% ROUGE-L, and 92.89% anti-hallucination rate, performing better than other existing state-of-the-art baseline models on three different benchmark databases. Conclusion: The secure authentication and regulatory compliance make HealthAssist a scalable and implementable system for real-world clinical deployment, with demonstrated clinical acceptability across expert panel evaluation.
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 0 citations
CIMAS HealthMate is proposed, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage.
Shamiso Simango, M. Mutandavari· Computer Science and Informa...· 0 citations
Digital health care platforms continue to cause an increase in global access to medical information, but they haven been fully able to facilitate access through continued fragmentation and lack of accessibility, as the majority of these systems use text-based descriptions of disease alone, with no capability of intelligent interpretation or interactivity and do not support multimodal forms of data (e.g., medical imaging). In this paper, we present a multimodal artificial intelligence-based platform for health care information retrieval and analysis called MedCare AI, which consists of three components: A curated knowledge base of 610 different diseases across 22 different categories from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC); An image analysis module that uses artificial intelligence (AI) to analyze scans for six different types of imaging technology, specifically: X-ray; computed tomography (CT); magnetic resonance imaging (MRI); ultrasound; positron emission tomography (PET); and electrocardiogram (ECG) imaging. Our analysis module utilizes a fine-tuned version of the ResNet-50 convolutional neural network (CNN) built on a defined preprocessing pipeline; A health care assistant that uses natural language processing to drive conversational interaction and uses a bi-directional long-short-term memory (BiLSTM)-based named entity recognition system. MedCare AI's performance outcomes were assessed on a dataset comprised of 500 queries, 300 conversations and 200 scans, achieving an average of 92.6%, 91.4%, 90.8%, and 91.1% on accuracy, precision, recall and F1 score respectively, when compared to current chat-based applications, demonstrating up to a 6.2% improvement over standalone chat-based applications. Robustness evaluation results identified less than 2.1% degradation of F1 score as a result of three differing levels of noise; demonstrating that the platform's capabilities as a multimodal integrative system meet the current gaps identified within existing literature, by providing access to disease knowledge retrieval, scan-based diagnostic and conversational interaction from a single easy to use Internet-based platform.
Parumanchala Bhaskar, A.Nageswari, B.Anjani Pranitha et al.· 2026 7th International Confe...· 0 citations
This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks, revealing that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning.
Angelower Santana-Velásquez, M. B. Salazar-Sánchez· Computers· 0 citations
By decoupling clinical information retrieval from generative chitchat, LENOHA enhances safety, preserves privacy, and markedly reduces energy use, offering a practical blueprint for sustainable and equitable medical AI deployment across diverse care settings.
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It is argued that MMAI is ethically novel in part because it renders cross-modal inferences as recordable data objects, thereby blurring the boundary between observation and generation, and argues for a shift from data-centric protection toward governance of inference and infrastructuring.
K. Kostick-Quenet, Jennifer K. Wagner, Laura Y. Cabrera et al.· AI and Ethics· 0 citations