Authors : Sahil Kadge; Kamran Khot; Yash Navander; Dr. Jayashree Khanapuri

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/ythu6rph

Scribd : https://tinyurl.com/5cp62pve

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN161

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

Abstract : In the evolving landscape of medical documentation, the necessity for efficient and accurate record-keeping systems is paramount, especially in specialised fields such as neurology where precision in terminology is crucial. This paper introduces a pioneering application of a fine-tuned Whisper model, specifically adapted for brain-related medical terms, integrated with an AI-driven system for automated template filling. The proposed system leverages advanced speech recognition technologies to capture doctors' verbal inputs and accurately transcribe these into designated report templates. The process simplifies the documentation workflow, significantly reducing the cognitive and administrative load on healthcare providers by enabling them to focus more on patient care rather than paperwork. Our research details the development and implementation of this innovative system, including the specific adaptations made to the Whisper model to enhance its accuracy with neurology-specific terminology. We also evaluate the system's performance in real-world medical settings and discuss the practical implications of integrating such AI tools in clinical practice. Furthermore, the system's capacity to generate ready-to- print PDF reports not only streamlines the documentation process but also ensures consistency and reliability in medical records. The overarching aim of this project is to demonstrate how targeted AI solutions can address the unique challenges of medical documentation, offering substantial benefits to healthcare providers and patients alike.

References :

1. Lee, H., & Kim, Y. (2024). "Whisper: An Effective Model for Transcribing Medical Speech." Journal of Medical Informatics, 20(3), 102-115. DOI: 10.5678/jmi. 2024.005 Chakravorty, H. (2020, February 29). To Detection of Fish Disease using Augmented Reality and Image Processing. Advances in Image and Video Processing, 8(1).
2. Jelinek, F. (1978). Continuous Speech Recognition for Text Applications. , 262-274.
3. Dayal, D. (2020). Review on Speech Recognition using Deep Learning. International Journal for Research in Applied Science and Engineering Technology.
4. Yu, Y. (2012). Research on Speech Recognition Technology and Its Application. 2012 International Conference on Computer Science and Electronics Engineering, 1, 306-309.
5. Alharbi, S., Alrazgan, M., Alrashed, A., Alnomasi, T., Almojel, R., Alharbi, R., Alharbi, S., Alturki, S., Alshehri, F., & Almojil, M. (2021). Automatic Speech Recognition: Systematic Literature Review. IEEE Access.