AI-POWERED MEDICAL ASSISTANCE WITH MACHINE LEARNING BASED IMAGE DIAGNOSIS
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The quick interpretation of medical imagery is crucial for timely clinical decision-making. However, healthcare systems around the world are facing a severe shortage of specialized radiologists. To tackle this issue, AI-driven diagnostic assistants have emerged as a practical solution to improve clinical workflows. While current deep learning models for medical image analysis often need extensive local computing power and experience high delays, cloud-based inference end- points provide remarkable scalability. This paper presents an AI-powered medical image chatbot designed to give instant, interactive diagnostic support. The proposed system uses a strong modular backend connected to state-of-the-art vision-capable Large Language Models (LLMs), specifically using LLaMA- based vision architectures, accessed through high-speed inference Application Programming Interfaces (APIs). By allowing users to easily upload complex medical images and ask questions in natural language, the system generates understandable diagnostic explanations and clinical insights in real time. The framework includes thorough image validation, secure data encoding proce- dures, and a dynamic model-fallback feature to ensure continuous service availability. Ultimately, this scalable application acts as a reliable tool for making expert-level medical image interpretation accessible to both healthcare professionals and patients, providing immediate, data-driven medical insights.
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Vardhan, T. V. & Ganesh, P. (2026). AI-Powered Medical Assistance with Machine Learning Based Image Diagnosis. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.630
Vardhan, T., and P. Ganesh. "AI-Powered Medical Assistance with Machine Learning Based Image Diagnosis." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.630.
Vardhan, T., and P. Ganesh. "AI-Powered Medical Assistance with Machine Learning Based Image Diagnosis." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.630.
References
2017.
2.Rajpurkar, J. Irvin, K. Zhu, et al., ”CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv preprint arXiv:1711.05225, 2017.
3.Gulshan, L. Peng, M. Coram, et al., ”Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA, vol. 316, no. 22, pp. 2402–2410, 2016.
4.Litjens, T. Kooi, B. E. Bejnordi, et al., ”A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.
5.S. W. Ting, C. Y. Cheung, G. Lim, et al., ”Devel- opment and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes,” JAMA, vol. 318, no. 22, pp. 2211–2223, 2017.
6.He, X. Zhang, S. Ren, and J. Sun, ”Deep Residual Learning for Image Recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
7.Ronneberger, P. Fischer, and T. Brox, ”U-Net: Con- volutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Interven- tion (MICCAI), pp. 234–241, 2015.
8.Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, ”Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1207–1216, 2016.
9.C. Shin, H. R. Roth, M. Gao, et al., ”Deep Convolu- tional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, 2016.
10.Tajbakhsh, J. Y. Shin, S. R. Gurudu, et al., ”Convo- lutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299–1312, 2016.
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