AI HEALTHCARE ASSISTANT USING MACHINE LEARNING AND NLP
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Department of Computer Science and Engineering, Sandip University Nashik
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Abstract
This paper presents an AI-based Healthcare Assistant that integrates Natural Language Processing (NLP), Machine Learning (ML), Optical Character Recognition (OCR), and Deep Learning techniques for preliminary medical guidance. The system processes user inputs including text symptoms, speech input, scanned medical reports, and chest X-ray images. BioBERT is used for symptom extraction, a machine learning classifier predicts possible diseases, Tesseract OCR digitizes medical reports, and a Convolutional Neural Network (CNN) analyzes X-ray images for pneumonia detection. Experimental evaluation shows 96.2% accuracy in symptom extraction, 94.5% disease prediction accuracy, 97.1% X-ray classification accuracy, and 95.8% OCR reliability. The proposed system aims to enhance healthcare accessibility in rural and underserved regions.
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Double-Blind Peer Review
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CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 02
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2
Article Type:
Research Article
Publication Date:
Feb 24 2026
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Suryawanshi, V. D. ,. K. N. S. ,. A. D. ,. (2026). AI Healthcare Assistant using Machine Learning and NLP. International Journal of Science, Strategic Management and Technology, 02(02). https://doi.org/10.55041/ijsmt.v2i2.001
Suryawanshi, Vaishnavi. "AI Healthcare Assistant using Machine Learning and NLP." International Journal of Science, Strategic Management and Technology, vol. 02, no. 02, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.001.
Suryawanshi, Vaishnavi. "AI Healthcare Assistant using Machine Learning and NLP." International Journal of Science, Strategic Management and Technology 02, no. 02 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.001.
References
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[4] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
[5] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
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[7] R. Miotto, F. Wang, S. Wang, X. Jiang, and J. Dudley, “Deep learning for healthcare: review, opportunities and challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2018.
[8] R. Smith, “An overview of the Tesseract OCR engine,” in Proceedings of the International Conference on Document Analysis and Recognition, 2007, pp. 629–633.
[9] D. Jurafsky and J. Martin, Speech and Language Processing, 3rd ed., Pearson, 2020.
[10] World Health Organization, “Digital health and artificial intelligence in healthcare systems,” WHO Technical Report, 2021.
[2] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[3] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT, 2019, pp. 4171–4186.
[4] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
[5] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
[6] A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019.
[7] R. Miotto, F. Wang, S. Wang, X. Jiang, and J. Dudley, “Deep learning for healthcare: review, opportunities and challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2018.
[8] R. Smith, “An overview of the Tesseract OCR engine,” in Proceedings of the International Conference on Document Analysis and Recognition, 2007, pp. 629–633.
[9] D. Jurafsky and J. Martin, Speech and Language Processing, 3rd ed., Pearson, 2020.
[10] World Health Organization, “Digital health and artificial intelligence in healthcare systems,” WHO Technical Report, 2021.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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