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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68598

MEDVISION AI: An AI-Assisted Medical Imaging Analysis and Clinical Report Summarization System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68598

Volume:

10 |

10 |

Oct 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68598

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

YJ

Yasharth Jadhav

Department of Computer Science and Engineering

MIT Art, Design and Technology University

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KT

Kartik Tanpure

Department of Computer Science and Engineering

MIT Art, Design and Technology University

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AG

Aniket Gonjari

Department of Computer Science and Engineering

MIT Art, Design and Technology University

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SS

Swayam Shah

Department of Computer Science and Engineering

MIT Art, Design and Technology University

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Mentors

PAU

Prof. Avinash Utikar

Department of Computer Science and Engineering

MIT Art, Design and Technology University

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Abstract

Medical imaging plays a key role in today's healthcare system, helping doctors spot and understand different diseases. But looking at medical images and reading the related reports can take a lot of time and needs expert knowledge. This paper introduces MEDVISION AI, an AI-powered healthcare tool that uses three special deep learning models to examine X-ray, CT, and MRI images. The system can find possible issues in the areas each model is trained for and gives doctors a first look at the results. It also handles medical PDF reports by pulling out important details and creating easy-to-read summaries, making it easier for doctors to get the information they need. The platform uses Python, FastAPI, HTML, CSS, JavaScript, and SQLite, with PyTorch or TensorFlow for building and running the AI models. Tools like NumPy, Pillow, and pydicom help process the images, while PyMuPDF is used for reading text from PDFs. The system is built with a modular design that keeps image analysis and document handling separate, but it offers a single place to upload files and check the results. The performance of the system can be measured using metrics like accuracy, precision, recall, specificity, and F1-score, along with how well it extracts and processes documents. MEDVISION AI is meant to speed up the initial review of medical information while ensuring that trained healthcare professionals remain in charge of making diagnoses and treatment plans.

KEYWORDS

Artificial intelligence medical image analysis deep learning X-ray classification CT scan MRI medical report summarization clinical decision support FastAPI SQLite.

Cite This Article

Yasharth Jadhav, Kartik Tanpure, Aniket Gonjari, Swayam Shah, Prof. Avinash Utikar, "MEDVISION AI: An AI-Assisted Medical Imaging Analysis and Clinical Report Summarization System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-9, Oct 2026. https://doi.org/10.55041/IJSREM68598

Full Article/Conclusion

MEDVISION AI is an AI-powered platform that combines the analysis of X-ray, CT, and MRI images with the extraction and summarization of medical PDFs. It uses Python, FastAPI, and SQLite to offer a single place for reviewing medical data.The system's effectiveness can be measured by classification results, how well text is extracted, the quality of summaries, and how fast it processes data.More testing is needed to ensure it works well in different healthcare settings.The system is meant to help doctors, not take the place of their judgment.

References

[1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998. [2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, vol. 25, 2012. [3] K. 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), 2016, pp. 770–778. [4] P. Rajpurkar et al., “CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning,” arXiv preprint arXiv:1711.05225, 2017. [5] X. Wang et al., “ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2097–2106. [6] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, pp. 234–241. [7] F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, vol. 18, pp. 203–211, 2021. [8] A. Dosovitskiy et al., “An image is worth 16×16 words: Transformers for image recognition at scale,” in Proc. International Conference on Learning Representations (ICLR), 2021. [9] A. E. W. Johnson et al., “MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports,” Scientific Data, vol. 6, Art. no. 317, 2019. [10] National Electrical Manufacturers Association, “Digital Imaging and Communications in Medicine (DICOM) Standard.” [Online]. Available: https://www.dicomstandard.org/

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Publication Date

10/31/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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