Enhancing Long-Term Care with Explainable AI: Improving Patient Outcomes, Caregiver Support, and Ethical Transparency

Author(s)	Bhanu Prakash Manjappasetty Masagali
Country	United States
Abstract	This paper explores the potential impact of Explainable Artificial Intelligence (XAI) on long-term care (LTC), highlighting the value of transparent and interpretable AI in supporting clinical decision-making, enhancing patient trust, and improving the quality of care. With an aging global population, LTC facilities are increasingly adopting AI tools to manage patient health and operational efficiency. XAI can play a critical role by ensuring that predictions and recommendations are understandable and actionable for caregivers and clinicians, fostering trust, accountability, and ethical use of AI in patient care.
Keywords	Explainable AI (XAI), Long-Term Care (LTC), Healthcare Decision-Making, Predictive Analytics in Healthcare, Patient Outcomes, Transparency in AI, AI in Elderly Care, Ethics in AI, Trust in AI, AI-Assisted Diagnosis, Caregiver Support, Patient-Centric AI Models, AI Interpretability, Human-AI Collaboration, Healthcare Personalization
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-11-19
Cite This	Enhancing Long-Term Care with Explainable AI: Improving Patient Outcomes, Caregiver Support, and Ethical Transparency - Bhanu Prakash Manjappasetty Masagali - IJFMR Volume 6, Issue 6, November-December 2024.

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