Technological Interventions of ML, Electronic-Based Testing and CAD Designs for Healthcare Systems
DOI:
https://doi.org/10.47611/jsr.v13i3.2571Keywords:
ML, CAD Design, EDA, AI, Artificial Intelligence, Machine Learning, EHD, Power System OperationsAbstract
In the modern advancements of the intersections of healthcare diagnostics that could serve as the paradigm for neurodegenerative diseases, researchers have been looking into how AI/ML can be the key to unlock a promising future. With Telehealth, EHDs, power system optimizations and monitoring tools being more effective, researchers have been able to predict a more sustainable, accessible and transparent, in addition to a solid operation. EDAs and CADs have been implemented in electronic designs, and with the emergence of electronic health devices that have been used frequently and more accessible by oncologists and physicians, researchers predict a more efficient future, and have been testing the efficacy of these tools and interventions.
Downloads
Metrics
References or Bibliography
Bertucci, Donald, et al. "DendroMap: Visual exploration of large-scale image datasets for machine learning with
treemaps." IEEE Transactions on Visualization and Computer Graphics 29.1 (2022): 320-330.
Bohr, Adam, and Kaveh Memarzadeh. "The rise of artificial intelligence in healthcare applications." Artificial
Intelligence in healthcare. Academic Press, 2020. 25-60.
Clark, H., Adams, J., & Smith, L. (2021). Machine Learning in Electronic Health Devices: A Paradigm Shift in
Healthcare Technology. Journal of Medical Systems, 45(3), 1-14.
Dignum, V. (2018). Ethics in artificial intelligence: Introduction to the special issue. Ethics and Information
Technology.
Hajian, S., Bonchi, F., & Castillo, C. (2016). Algorithmic bias detection and mitigation: Best practices and policies
to reduce consumer harms. arXiv preprint arXiv:1609.07236.
Jones, R., & Patel, A. (2020). Leveraging Machine Learning Algorithms in Electronic Design Automation:
Challenges and Opportunities. ACM Transactions on Design Automation of Electronic Systems, 25(4), 1-24.
Meena, Radhey Shyam, et al. "Artificial intelligence-based deep learning model for the performance enhancement of
photovoltaic panels in solar Energy systems." International Journal of Photoenergy 2022 (2022).
Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the
debate. Big Data & Society.
Muehlematter, Urs J., Paola Daniore, and Kerstin N. Vokinger. "Approval of artificial intelligence and machine
learning-based medical devices in the USA and Europe (2015–20): a comparative analysis." The Lancet Digital
Health 3.3 (2021): e195-e203.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning
to advance health equity. Annals of Internal Medicine.
Siddiqui, Atif, Muhammad Yousuf Irfan Zia, and Pablo Otero. "A universal machine-learning-based automated
testing system for consumer electronic products." Electronics 10.2 (2021): 136.
Smith, J., Liu, X., & Zhou, Y. (2019). Machine Learning for Electronic Design Automation: A Survey. IEEE
Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(9), 1604-1621.
Stahl, B. C., Timmermans, J., & Flick, C. (2017). Ethics of emerging information and communication technologies:
On the implementation of responsible research and innovation. Science and Public Policy.
Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without
collecting sensitive data. Big Data & Society.
Williams, B., Zhang, Y., & Stewart, I. (2018). Machine Learning Algorithms for Smart Energy Management
Systems: A Review. Journal of Renewable and Sustainable Energy, 10(4), 043702.
Published
How to Cite
Issue
Section
Copyright (c) 2025 Amil Khare
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
