Reclaiming XAI as an Innovation in Healthcare: Bridging Rule-Based Systems

Abstract

The adoption of explainable artificial intelligence (XAI) in healthcare has been increasingly framed as dependent on transparency, trustworthiness, and accountability. The objective of this study was the reclamation of rule-based systems within XAI as innovations aligned with healthcare accountability. A scientometric mapping analysis was conducted using publications indexed in Scopus between 1 January 2018 and 20 May 2025. The search strategy was applied to 1034 records. From these, 892 were screened, 238 duplicates were removed, and 654 studies were retained in accordance with the PRISMA 2020 framework. Thematic cluster analysis, co-authorship structures, and keyword co-occurrence patterns were visualised through VOSviewer 1.6.20. Transparency, accountability, and trustworthiness were established as central values for clinical integration. Expanding domains were identified in smart healthcare, digital health, healthcare technology, and mHealth, while interpretability was observed to remain underrepresented. Rule-based systems, frequently in hybrid forms, were demonstrated to bridge algorithmic complexity with interpretability. This bridging was interpreted as reinforcing physician confidence, regulatory compliance, and patient safety. It was concluded that the advancement of XAI in healthcare has been shaped by the interplay of ethical principles, methodological innovation, and digital health applications. Practical implications, theoretical contributions, and potential limitations were systematically addressed.