Jul 2026· Theoretical and Natural Science· Vol 177, pp. 246-252· 0 citations
TL;DR
Future directions encompass multimodal diagnostic integration, transfer learning with foundation models, large language model (LLM)-assisted decision-making decision-making, and a "human-animal-environment" intelligent prevention and control system.
Abstract
Schistosomiasis is a serious zoonotic parasitic disease affecting approximately 250 million people worldwide. Despite significant progress in China, challenges remain in snail surveillance, the low sensitivity of traditional diagnostics, and imprecise resource allocation. Recent advances in artificial intelligence offer new solutions. In snail monitoring, CNNs achieve over 90% accuracy in image recognition, and deep learning combined with remote sensing accurately identifies snail habitats. For diagnosis, mobile microscopy integrated with deep learning enables automated on-site egg detection. In strategy optimization, machine learning models (e.g., XGBoost) quantitatively evaluate intervention cost-effectiveness, and decision support systems simulate integrated control effects. Challenges include data quality, accessibility, and ethical privacy concerns. Future directions encompass multimodal diagnostic integration, transfer learning with foundation models, large language model (LLM)-assisted decision-making decision-making, and a "human-animal-environment" intelligent prevention and control system.
Artificial intelligence has considerable potential to improve NTD diagnosis in low-resource settings, but successful adoption depends on trust, transparency, and usability, so a proposed framework provides a structured pathway for developing explainable AI systems that are technically robust, clinically meaningful, ethically responsible, and implementable within resource-constrained health systems.
D. C. Innocent, Rejoicing Chijindum Innocent, Increase Praise Innocent· Frontiers in Digital Health· 0 citations
Malaria endures a significant part in public health concern, especially in tropical and subtropical regions. Traditional malaria control methods often face limitations with surveillance, diagnosis and efficient resource allocation. This review explores the role of Artificial Intelligence (AI) in augmenting data-driven decision-making for malaria control and elimination efforts, focusing on surveillance systems, enhancing the effectiveness of intervention strategies and optimizing the resource allocations. AI technologies, mainly machine learning algorithms and computer vision systems, demonstrate significant potential in improving malaria control outcomes. Key findings include increased accuracy in outbreak prediction, improved diagnostic precision through automated microscopy and optimized resource allocation reducing response times. Additionally, deep learning models are emerging as promising tools in identifying drug resistance patterns and personalizing treatment protocols. AI integration in malaria control programs offers substantial benefits for public health decision-making. In this article, we conducted a comprehensive review of peer-reviewed literature, analyzing AI applications in malaria control across key domains such as surveillance, diagnosis, treatment and resource management. However, effective implementation requires robust data infrastructure, ethical frameworks addressing algorithmic bias and sustained international collaboration. Future directions prioritize equitable access, capacity building and development of standardized evaluation metrics for evaluating AI-driven interventions.
Sweta Bhan, Ayushi Singh, Pankaj U. Ramteke et al.· International Journal of Com...· 0 citations
Paddy is one of the key staple crops which support the livelihood of billions of people throughout the world. However, the productivity of this crop is constantly under threat from fungal, bacterial, viral and nutritional associated illnesses which cause huge losses in production and economy. Thus, early and precise diagnosis of illness is important for effective crop management and sustained agricultural production. Recently, automatic detection of paddy diseases has been improved greatly with the rapid progress of artificial intelligence (AI), computer vision, deep learning, remote sensing, Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV)-based monitoring. In this survey, the recent works in intelligent diagnosis of paddy diseases have been comprehensively studied by considering conventional diagnosis approaches, machine learning approaches and deep learning approaches including convolutional neural networks, vision transformers, hybrid models and explainable AI frameworks. The paper also summarises the publicly accessible datasets, picture pre-processing methods, feature extraction approaches, classification algorithms, assessment metrics and deployment platforms for field-level disease monitoring. The previous studies are compared for diagnostic performance, computational complexity, resilience, and practical usefulness under real-world agricultural situations. The survey also addresses the present issues such as scarce and imbalanced data, environmental variations, similarity of disease symptoms, model interpretability, generalisation across domains, and deployment with limited resources. Finally, we discuss current research themes like as multimodal data fusion, federated learning, foundation models, edge intelligence, digital twins and autonomous precision agriculture as interesting approaches for next generation paddy disease diagnosis systems. This assessment, by summarising recent developments and pointing out crucial research voids, is an important reference for researchers, agricultural engineers, and practitioners aiming to find accurate, scalable and intelligent solutions for crop disease management.
M. Priya, Dr. S. Ashok Kumar· International Journal of Sci...· 0 citations
It is argued that artificial intelligence encompassing machine learning and deep learning can help shift malaria control from reactive reporting toward predictive, precision public health, while cautioning that it is one enabler among many rather than a stand-alone solution.
M. S. Abdi, Abdirahman Mohamed Adan, N.I. Ahmed et al.· Frontiers in Digital Health· 0 citations
Background. Foodborne diseases (FBDs) constitute a significant public health issue: the WHO estimates 600 million cases, 420,000 deaths, and 33 million DALYs annually. Traditional surveillance systems suffer from delays in outbreak recognition, under-reporting, and data fragmentation. Artificial intelligence (AI) is emerging as a strategic tool for the prevention and management of FBDs.
Materials and Methods. A systematic review of peer-reviewed literature (January 2019 – April 2026) was conducted on PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, using MeSH terms such as "artificial intelligence", "machine learning", "foodborne diseases", and "outbreak detection". Original studies, reviews, meta-analyses, and reports from the WHO, EFSA, and FDA in English and available as free full text were included. Study selection and methodological quality were evaluated using the PRISMA protocol.
Results. Out of 847 identified articles, 62 were included in the analysis. Deep Learning (DL) models applied to hyperspectral imaging achieve a sensitivity of >94% for Salmonella, Listeria monocytogenes, and E. coli. AI systems demonstrated the ability to anticipate outbreak detection by approximately 4.2 days.
Conclusions. AI is a promising tool to transform FBD surveillance. Its implementation requires dataset standardization, privacy protection, the reduction of algorithmic bias, and harmonized regulatory frameworks. Future research must focus on prospective validation in real-world contexts, algorithmic equity, and integration with national surveillance infrastructures.
Unknown authors· Journal of Biological Resear...· 0 citations
Poultry farming is a critical component of global food security, yet it is highly vulnerable to fast spreading infectious diseases such as coccidiosis, salmonellosis and Newcastle disease. Conventional diagnostic pathways depend on clinical examination and laboratory tests, which are time consuming, expertise dependent and often inaccessible for small and medium scale farmers. This paper presents a combined survey and implementation of PoultryGuard, a smartphone-centric framework for early poultry disease detection from chicken fecal images using convolutional neural networks. First, recent progress in image-based poultry disease detection is reviewed, with emphasis on fecal-image classification and transfer learning strategies. Then, the design and implementation of an EfficientNet-B3 based classifier trained on 8,067 annotated fecal images across four classes (healthy, coccidiosis, salmonella and Newcastle disease) is described. The model is deployed inside a Streamlit application that integrates user authentication, real time image acquisition, a structured veterinary knowledge base, automated diagnostic report generation in PDF format, Telegram based emergency alerts, a veterinary hospital locator and an admin analytics dashboard. Experimental evaluation on a heldout test set shows that the implemented EfficientNet-B3 model achieves 93.73% accuracy, with macro-averaged precision, recall and F1-scores above 0.93, while maintaining low inference latency suitable for smartphone assisted deployment. The study shows that poultry disease management in resource constrained farm environments can be revolutionized by combining deep learning with clinical decision support and communication modules.
S. K, D. S, Deeksha V Panchal et al.· 2026 International Conferenc...· 0 citations
