Healthcare Through AI: Integrating Deep Learning, Federated Learning, and XAI for Disease Management
Vinoth Kumar Kolluru1, Yudhisthir Nuthakki2, Sudeep Mungara3, Sonika Koganti4, Advaitha Naidu Chintakunta5, Charan Sundar Telaganeni6
1Vinothkumar Kolluru, Department of Data Science, Stevens Institute of Technology, Seattle, WA, (United States of America) USA.
2Yudhisthir Nuthakki, Department of Computer Science, Silicon Valley University, Indianapolis, Indiana, (United States of America) USA.
3Sudeep Mungara, Stevens Institute of Technology, Philadelphia, PA, (United States of America) USA.
4Sonika Koganti, Department of Computer Science, Sacred Heart University, Indianapolis, Indiana, (United States of America) USA.
5Advaitha Naidu Chintakunta, Department of Computer Science, University of North Carolina at Charlotte, Seattle, WA, (United States of America) USA.
6Charan Sundar Telaganeni, Department of Computer Science, Stevens Institute of Technology, Texas, TX, (United States of America) USA.
Manuscript received on 04 January 2024 | Revised Manuscript received on 10 January 2024 | Manuscript Accepted on 15 January 2024 | Manuscript published on 30 January 2024 | PP: 21-27 | Volume-13 Issue-6, January 2024 | Retrieval Number: 100.1/ijsce.D364614040924 | DOI: 10.35940/ijsce.D3646.13060124
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Healthcare, Artificial Intelligence, Deep Learning, Federated Learning, Disease Management
Scope of the Article: Artificial Intelligence