# Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2402.07915 · HTML: https://mlsanity.com/paper/2402.07915/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network](https://mlsanity.com/paper/2209.03166/) (2022)
- [Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals](https://mlsanity.com/paper/2301.06923/) (2023)

## Built on by (most cited first, 3 of 3)

- [LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2403.08822/) (2024)
- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)
- [RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling](https://mlsanity.com/paper/2408.06665/) (2024)

## Abstract (arXiv)

This paper proposed a comprehensive mixed-methods framework with varied samples of older adults, including user experience, usability assessments, and in-depth interviews with the integration of Explainable Artificial Intelligence (XAI) methods. The experience of older adults' interaction with the Ehealth interface is collected through interviews and transformed into operatable databases whereas XAI methods are utilized to explain the collected interview results in this research work. The results show that XAI-infused e-health interfaces could play an important role in bridging the age-related digital divide by investigating elders' preferences when interacting with E-health interfaces. Furthermore, the study identifies important design factors, such as intuitive visualization and straightforward explanations, that are critical for creating efficient Human Computer Interaction (HCI) tools among older users. Furthermore, this study emphasizes the revolutionary potential of XAI in e-health interfaces for older users, emphasizing the importance of transparency and understandability in HCI-driven healthcare solutions. This study's findings have far-reaching implications for the design and development of user-centric e-health technologies, intending to increase the overall well-being of older adults.