Authors : Hazel Galas Lampitoc; Reagan B. Ricafort

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/3y7mtara

Scribd : https://tinyurl.com/5azkcur7

DOI : https://doi.org/10.38124/ijisrt/26jun434

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Intelligent systems can be deployed for managing biomedical images in distributed healthcare environments for early disease diagnosis. Standard centralized architectures are often limited in scalability, interoperability, and metadata consistency, which is an issue when it comes to efficient diagnostic workflows. This article presents a web-based retrievaldriven artificial intelligence framework tailored specifically for distributed biomedical imaging platforms. The research adopts a conceptual research design (theory-based approach), with a cross between theoretical modelling, structured simulation and the analytical approach of reasoning to construct and evaluate the framework. Modeling with componentbased data was used to specify embedding generation, similarity scoring mechanism, metadata schema, distributed node behavior, and federated merging logic. In order to simulate retrieval behavior across distributed nodes, conceptual diagnostic test cases — e.g., glioma detection, pneumonia classification, normal-versus-abnormal comparisons—were generated. They showed stable clustering of conceptual embeddings, consistency in similarity scoring across nodes and coherent aggregation of retrieval outputs through federated merging. Metadata standardization also enhanced the interpretability of this image along with conceptual retrieval conflicts between them, and also the overall architecture provided logical scalability that was suitable for distributed biomedical imaging platforms. All in all, the study finds that this framework provides a strong, theoretically solid foundation that supports deployment of retrieval-driven AI in distributed healthcare environments. While conceptual, this model draws attention to substantial opportunities for scalable and interoperable, interpretable retrieval architectures enabling early disease diagnosis, so future work should be focused on empirical validation of such an infrastructure as well as prototype development and evaluation by experts working in this area to accelerate the approach toward practical implementation.

Keywords : Biomedical Imaging, Retrieval-Driven AI, Distributed Systems, Embedding-Based Feature Extraction, Similarity Scoring, Metadata Standardization, Federated Retrieval, Early Disease Diagnosis.

References :

1. Choudhury, O., Karmakar, S., & Chowdhury, M. S. (2020). Cloud-based framework for real-time biomedical data analytics. Computers in Biology and Medicine, 123, 103867. https://doi.org/10.1016/j.compbiomed.2020.103867
2. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
3. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., … Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
4. García-Gómez, J. M., Ortega, M. V. T., & García-Sáez, G. (2019). The role of artificial intelligence in precision medicine. Expert Opinion on Drug Discovery, 14(2), 111–114. https://doi.org/10.1080/17460441.2019.1568700
5. Gupta, D., Sharma, A., & Rani, R. (2020). Web-based AI framework for remote diagnosis using biomedical images. International Journal of Medical Informatics, 141, 104203. https://doi.org/10.1016/j.ijmedinf.2020.104203
6. Hinton, G., & Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647
7. Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., … Feng, J. (2020). Development and evaluation of an AI system for COVID‑19 diagnosis. Nature Communications, 11(1), 5088. https://doi.org/10.1038/s41467-020-18685-1
8. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
9. Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., … Keane, P. A. (2019). A comparison of deep learning performance against healthcare professionals in detecting diseases from medical imaging: A systematic review and meta‑analysis. The Lancet Digital Health, 1(6), e271–e297. https://doi.org/10.1016/S2589-7500(19)30123-2
10. Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018). Survey on deep learning for radiotherapy. Computers in Biology and Medicine, 98, 126–146. https://doi.org/10.1016/j.compbiomed.2018.05.018
11. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., … Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225. https://doi.org/10.48550/arXiv.1711.05225
12. Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
13. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. https://doi.org/10.1109/TMI.2016.2535302

14. Topol, E. (2019). High‑performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7