Explainable AI (EXAI) for Smart Healthcare Automation

Introduction

Integrating AI into healthcare systems has revolutionized medical services. Smart healthcare automation, empowered by AI, offers opportunities for improved diagnostics, personalized treatment, efficient resource allocation, and enhanced patient outcomes (Ha et al., 2022). AI algorithms have shown remarkable capabilities in analyzing vast amounts of medical data, identifying patterns, and making predictions (Mehta & Passi, 2022). The black-box nature of many AI models poses challenges in the healthcare domain, where transparency and explainability are paramount. Smart healthcare automation, driven by AI algorithms, has the potential to streamline healthcare processes, improve accuracy, and enhance the delivery of personalized medicine (Ryo, 2022). However, adopting AI in healthcare is challenging, particularly regarding transparency and explainability. XAI can enhance accountability and facilitate better decision-making by enabling healthcare professionals and patients to understand and trust AI-driven decisions. We also address the ethical considerations and challenges associated with implementing XAI in healthcare settings, including privacy, bias, and regulatory implications.

In many healthcare applications, the decisions made by AI systems have far-reaching consequences on patients' lives. Therefore, it is essential to understand the underlying rationale behind these decisions, especially considering the potential risks associated with AI-based recommendations (Baum et al., 2022; Owens et al., 2022). Unfortunately, the black-box nature of AI algorithms, which often function as complex mathematical models, poses a significant hurdle in providing clear explanations for their outputs. This lack of transparency and interpretability can hinder the trust, acceptance, and ethical use of AI systems in healthcare (Pérez-Landa et al., 2021). XAI has emerged as a vital research area that addresses this challenge by providing interpretable and transparent AI models (Hu et al., 2021). XAI techniques enable healthcare professionals, patients, and other stakeholders to understand how AI systems arrive at their conclusions, allowing them to assess the decisions' reliability. By unraveling the black box of AI, XAI can enhance the interpretability, trustworthiness, and accountability of AI systems in healthcare applications (Dazeley et al., 2021).

By enabling transparent XAI systems, healthcare providers can gain insights into decision-making, ensuring the responsible use of AI algorithms (Tjoa & Guan, 2021). In addition, patients can also benefit from increased trust and understanding of the AI-driven recommendations, empowering them to actively participate in their own healthcare journey. Ultimately, integrating XAI into smart healthcare automation can revolutionize healthcare delivery, improve patient outcomes, and facilitate the efficient allocation of healthcare resources (Schwalbe & Finzel, 2023). While AI has demonstrated great potential in healthcare, its lack of explainability hampers its widespread adoption. In critical healthcare applications, such as medical diagnosis and treatment recommendation systems, it is crucial to understand how AI arrives at its decisions. The ability to explain AI-based recommendations not only enhances trust among healthcare providers and patients but also aids in identifying potential biases, ensuring ethical use, and improving overall system performance (Dazeley et al., 2021; Hu et al., 2021; Schwalbe & Finzel, 2023). Therefore, there is a pressing need to explore and develop methods for XAI tailored explicitly for smart healthcare automation.