The project is the medical diagnostic system, powered by AI and based on the Convolutional Neural Networks (CNNs), that automatically identifies abnormalities in medical images with a high precision that is expected to enhance the accuracy of the diagnosis, ease the workload of the medical specialists, and offer scalable, reliable, and available AI-based healthcare assistance systems to hospitals and other clinical settings.
A comprehensive narrative review of current AI/DL applications in ultrasound diagnosis, synthesising evidence across four major clinical domains and identifying recurring limitations across the literature - dataset heterogeneity, limited external/multicentre validation, and interpretability gaps - and outlines directions for future research toward clinically deployable, trustworthy AI-assisted ultrasound diagnosis.
R. Sivakumar, Arati Shahapurkar, J. G et al.· Adolescência e Saúde· 0 citations
This comprehensive review systematically examines the architecture, functionality, and clinical effectiveness of ANN-based models applied to a diverse spectrum of medical imaging modalities, encompassing mammography, magnetic resonance imaging (MRI), computed tomography (CT), fundus photography, and dermoscopy.
Maryam Omar Al-Tohamy Abdel Hamid, A. Arjiah, Anaas Mansour Abdulsalam· Al-Farooq Journal of Science...· 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.
P. Bhaskar, A.Nageswari, B.Anjani Pranitha et al.· 2026 7th International Confe...· 0 citations
BACKGROUND
Current artificial intelligence (AI) models for medical imaging predominantly focus on a single imaging modality and a single disease. Attempts to create multimodal and multi-disease models have resulted in inconsistent clinical accuracy. Furthermore, training these models typically requires large, well labelled datasets, which are costly and labour intensive to prepare. We aimed to train and evaluate an AI model that can interpret diverse imaging modalities across specialties while maintaining robust performance within each modality.
METHODS
We developed Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM), a multi-specialty model trained using self-supervised learning and a memory module. MerMED-FM was pretrained on publicly sourced, unlabelled medical images from 12 specialties and seven imaging modalities: chest x-rays, CT, ultrasound, histopathology, colour fundus photography (CFP), optical coherence tomography (OCT), and dermatoscopy. After pretraining, the model was fine-tuned, validated, and evaluated for the diagnosis of a range of diseases on 26 public datasets and five private datasets comprising radiology, histopathology, and ophthalmology images. MerMED-FM was compared against a general-domain vision foundation model, various specialist single-modality foundation models, and a multispecialty foundation model. Models were fine-tuned using 10%, 30%, 50%, and 100% of data, with primary comparative analyses conducted using a 10% label fraction. The primary outcome was the area under the receiver operating characteristic curve (AUROC), which was summarised by imaging modality.
FINDINGS
MerMED-FM was trained on around 3·3 million images from 53 publicly available, unlabelled datasets, comprising 713 931 chest x-rays, 292 353 CT slices, 389 885 ultrasound frames, 1 017 712 pathology patches, 333 099 CFP images, 176 719 OCT slices, and 401 059 dermatoscopy images. Strong performance was achieved across all modalities at a label fraction of only 10%, with mean AUROC values of 0·844 for chest x-rays, 0·906 for CT, 0·818 for ultrasound, 0·908 for histopathology, 0·810 for CFP, 0·962 for OCT, and 0·827 for dermatoscopy.
INTERPRETATION
MerMED-FM has the potential to be a highly adaptable, versatile, cross-specialty foundation model that enables robust interpretation of medical imaging across diverse medical disciplines.
FUNDING
National Medical Research Council, Singapore and the Agency for Science, Technology and Research, Singapore.
Yang Zhou, C. Quek, Jun Zhou et al.· The Lancet Digital Health· 1 citation
The research introduces a dimension in which medical specialists can have real-time conversations with a trained LLM model that leverages knowledge from a medical encyclopedia, and enhances collaboration between AI and medical professionals, creating a platform for exchanging knowledge.
M. I. Ahmed· Journal of Intelligent Decis...· 1 citation
Artificial intelligence (AI)-based medical imaging diagnosis has demonstrated remarkable performance across multiple clinical domains, with deep learning models frequently reporting diagnostic accuracy, sensitivity, and specificity exceeding 90% under controlled experimental conditions. However, translating these results into clinically reliable, regulatory-compliant systems remains a critical challenge. As a narrative survey rather than an original benchmark study, this paper reports no new experimental results; instead, it introduces a modality-aware analytical framework organizing the existing literature across four dimensions: imaging modality, data provenance, validation maturity, and model architecture. Using this taxonomy, the survey synthesizes unimodal and multimodal fusion approaches spanning radiology (CT, MRI, X-ray), pathology (whole-slide images), ophthalmology (fundus photography, OCT), and multi-source fusion combining imaging with electronic health records (EHR) and genomic data. The synthesis indicates that high reported accuracy is strongly contingent on data characteristics and evaluation conditions, with many models relying on low-maturity validation lacking evidence of generalization in real-world settings. To address these limitations, an engineering-oriented deployment framework is proposed, integrating modality-driven model selection, structured preprocessing pipelines, multi-level clinical validation, computational feasibility assessment, and explainability, together with a clinical deployment readiness model spanning validation maturity, data diversity, interpretability, and regulatory alignment. Key challenges include the single-site generalization gap, algorithmic bias across demographic groups, limited clinical adoption of explainable AI, insufficient alignment with regulatory frameworks including FDA 510(k), De Novo, and EU MDR/IVDR pathways, and a continuing need for prospective multicenter validation. Future directions toward federated learning, foundation models, certification-aware design, and multimodal digital biomarker integration are outlined.
Enoch Jacob Dodo, Amos Takai Yayock, Gregory Onwodi et al.· Journal of Science Research...· 0 citations
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