Healthcare Through AI: Integrating Deep Learning, Federated Learning, and XAI for Disease Management
Article Sidebar
Main Article Content
Abstract
The applications of Artificial Intelligence (AI) have been resonating across various fields for the past three decades, with the healthcare domain being a primary beneficiary of these innovations and advancements. Recently, AI techniques such as deep learning, machine learning, and federated learning have been frequently employed to address challenges in disease management. However, these techniques often face issues related to transparency, interpretability, and explainability. This is where explainable AI (XAI) plays a crucial role in ensuring the explainability of AI models. There is a need to explore the current role of XAI in healthcare, along with the challenges and applications of XAI in the domain of healthcare and disease management. This paper presents a systematic literature review-based study to investigate the integration of XAI with deep learning and federated learning in the digital transformation of healthcare and disease management. The results of this study indicate that XAI is increasingly gaining the attention of researchers, practitioners, and policymakers in the healthcare domain.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Kinger, S., & Kulkarni, V. (2021, August). Explainable AI for deep learning-based disease detection. In Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing (pp. 209-216). https://doi.org/10.1145/3474124.3474154
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022). Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge. IEEE Open Journal of the Computer Society, 3, 172-184. https://doi.org/10.1109/OJCS.2022.3206407
Van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2022). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 79, 102470. https://doi.org/10.1016/j.media.2022.102470
Kim, K. H., Koo, H. W., Lee, B. J., Yoon, S. W., & Sohn, M. J. (2021). Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning. Journal of the Korean Physical Society, 79(3), 321-327. https://doi.org/10.1007/s40042-021-00202-2
Nuthakki, S., Bhogawar, S., Venugopal, S. M., & Mullankandy, S. "Conversational AI AND LLM'S Current And Future Impacts In Improving And Scaling Health Services," International Journal of Computer Engineering and Technology (IJCET), Vol. 14, no. 3, pp.149-155, Dec. 2023, https://iaeme.com/Home/issue/IJCET?Volume=14&Issue=3
Kavya, R., Christopher, J., Panda, S., & Lazarus, Y. B. (2021). Machine learning and XAI approaches for allergy diagnosis. Biomedical Signal Processing and Control, 69, 102681. https://doi.org/10.1016/j.bspc.2021.102681
La Ferla, M. (2023, June). An XAI Approach to Deep Learning Models in the Detection of DCIS. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 409-420). Springer https://doi.org/10.1007/978-3-031-34171-7_33
Nuthakki, S., Neela, S., Gichoya, J. W., & Purkayastha, S. (2019). Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks. arXiv preprint arXiv:1912.12397.
Ward, I. R., Wang, L., Lu, J., Bennamoun, M., Dwivedi, G., & Sanfilippo, F. M. (2021). Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes? Computer Methods and Programs in Biomedicine, 212, 106415. https://doi.org/10.1016/j.cmpb.2021.106415
Nuthakki, S., Kolluru, V. K., Nuthakki, Y., & Koganti, S. “Integrating Predictive Analytics and Computational Statistics for Cardiovascular Health Decision-Making”, International Journal Of Innovative Research And Creative Technology, vol. 9, no. 3, pp. 1-12, May 2023, doi: https://doi.org/10.5281/zenodo.11366389
Bárcena, J. L. C., Ducange, P., Marcelloni, F., Renda, A., & Ruffini, F. (2023, July). Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson’s Disease Progression. In World Conference on Explainable Artificial Intelligence (pp. 630-648). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_34
Nuthakki, S., Kumar, S., Kulkarni, C. S., & Nuthakki, Y. (2022). “Role of AI Enabled Smart Meters to Enhance Customer Satisfaction”. International Journal of Computer Science and Mobile Computing, Vol.11 Issue.12, December- 2022, pg. 99-107, doi: https://doi.org/10.47760/ijcsmc.2022.v11i12.010 https://doi.org/10.47760/ijcsmc.2022.v11i12.010
Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., ... & Abdulkadir, A. (2023). Explainable, domain-adaptive, and federated artificial intelligence in medicine. IEEE/CAA Journal of Automatica Sinica, 10(4), 859-876. https://doi.org/10.1109/JAS.2023.123123
Singh, D., Nuthakki, S., Naik, A., Mullankandy, S., Singh, P. K., & Nuthakki, Y. (2022). “Revolutionizing Remote Health: The Integral Role of Digital Health and Data Science in Modern Healthcare Delivery”, Cognizance Journal of Multidisciplinary Studies, Vol.2, Issue.3, March 2022, pg. 20-30, doi: https://10.47760/cognizance.2022.v02i03.002 https://doi.org/10.47760/cognizance.2022.v02i03.002
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences, 11(11), 5088. https://doi.org/10.3390/app11115088
Giuste, F., Shi, W., Zhu, Y., Naren, T., Isgut, M., Sha, Y., ... & Wang, M. D. (2022). Explainable artificial intelligence methods in combating pandemics: A systematic review. IEEE Reviews in Biomedical Engineering, 16, 5-21. https://doi.org/10.1109/RBME.2022.3185953
Kathiriya, S., Nuthakki, S., Mulukuntla, S., & Charllo, B. V. “AI and The Future of Medicine: Pioneering Drug Discovery with Language Models”, International Journal of Science and Research, vol. 12, no. 3, pp. 1824-1829, Mar. 2023, doi: https://dx.doi.org/10.21275/SR24304173757 https://doi.org/10.21275/SR24304173757
Kolluru, V., Mungara, S., & Chintakunta, A. N. (2019). Securing the IoT ecosystem: Challenges and innovations in smart device cybersecurity. International Journal on Cryptography and Information Security (IJCIS), 9(1/2), 37. https://doi.org/10.5121/ijcis.2019.9203 https://doi.org/10.5121/ijcis.2019.9203
Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine, 226, 107161. https://doi.org/10.1016/j.cmpb.2022.107161
Band, S. S., Yarahmadi, A., Hsu, C. C., Biyari, M., Sookhak, M., Ameri, R., ... & Liang, H. W. (2023). Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods. Informatics in Medicine Unlocked, 101286. https://doi.org/10.1016/j.imu.2023.101286
D Singh, S Bhogawar, S Nuthakki, N Ranganathan, "Enhancing Patient-Centered Care in Oncology through Telehealth: Advanced Data Analytics and Personalized Strategies in Breast Cancer Treatment", International Journal of Science and Research (IJSR), Volume 10 Issue 9, September 2021, pp. 1707-1715, https://www.ijsr.net/getabstract.php?paperid=SR24108012724 https://doi.org/10.21275/SR24108012724
Alsaleh, M. M., Allery, F., Choi, J. W., Hama, T., McQuillin, A., Wu, H., & Thygesen, J. H. (2023). Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. International Journal of Medical Informatics, 105088. https://doi.org/10.1016/j.ijmedinf.2023.105088
Allgaier, J., Mulansky, L., Draelos, R. L., & Pryss, R. (2023). How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare. Artificial Intelligence in Medicine, 143, 102616. https://doi.org/10.1016/j.artmed.2023.102616
Gupta, J., & Seeja, K. R. (2024). A Comparative Study and Systematic Analysis of XAI Models and their Applications in Healthcare. Archives of Computational Methods in Engineering, 1-26. https://doi.org/10.1007/s11831-024-10103-9
Sharma, P., & Site, S. (2022). A Comprehensive Study on Different Machine Learning Techniques to Predict Heart Disease. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 2, Issue 3, pp. 1–7). https://doi.org/10.54105/ijainn.c1046.042322
Sutabri, T., Selvam, R. P., Shankar, K., Nguyen, P. T., Hashim, W., & Maseleno, A. (2019). Machine Learning for Healthcare Diagnostics. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6s2, pp. 999–1001). https://doi.org/10.35940/ijeat.f1304.0886s219
Ali, N. shabaz, & Divya, G. (2020). Prediction of Diseases in Smart Health Care System using Machine Learning. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 5, pp. 2534–2537). https://doi.org/10.35940/ijrte.e6482.018520
Venkatesan, M., Kulkarni, Anju. V., Menon, R., & Ramakrishnan, V. (2020). A Comprehensive Big Data Analytics Based Framework for Premature Recognition of Breast Cancer. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 3, pp. 2188–2192). https://doi.org/10.35940/ijitee.c8793.019320
Sistla, S. (2022). Predicting Diabetes u sing SVM Implemented by Machine Learning. In International Journal of Soft Computing and Engineering (Vol. 12, Issue 2, pp. 16–18). https://doi.org/10.35940/ijsce.b3557.0512222