Informatics

Financial Technology (FinTech) is fundamentally reshaping the accounting profession, accelerating the shift from routine transactional activities to more strategic, data-driven functions. This transformation demands advanced digital competencies, yet the scholarly understanding of these skills remains fragmented. To provide conceptual and analytical clarity, this study defines FinTech as an ecosystem of enabling technologies, including artificial intelligence, data analytics, and blockchain, that collectively drive this professional transition. Addressing the lack of systematic synthesis, the study employs a systematic literature review (SLR) guided by the PRISMA 2020 framework, complemented by bibliometric analysis, to map the intellectual landscape. The review focuses on peer-reviewed journal articles published between January 2020 and June 2025, thereby capturing the accelerated digital transformation of the post-pandemic era. The analysis identifies four dominant thematic clusters: (1) the professional context and digital transformation; (2) the educational response and curriculum development; (3) core competencies and their technological drivers; and (4) ethical judgement and professional responsibilities. Synthesising these themes reveals critical research gaps in faculty readiness, curriculum integration, ethical governance, and the empirical validation of institutional strategies. By offering a structured map of the field, this review contributes actionable insights for educators, professional bodies, and firms, and advances a forward-looking research agenda to align professional readiness with the realities of the FinTech era.

Artificial intelligence (AI) is becoming increasingly significant in healthcare around the world, especially in China, where rapid population ageing coincides with rising expectations for quality of life and a shrinking care workforce. This study explores Chinese health practitioners’ perspectives on using AI assistants in integrated geriatric and palliative care. Drawing on Actor–Network Theory, care is viewed as a network of interconnected human and non-human actors, including practitioners, technologies, patients and policies. Based in Northeast China, a region with structurally marginalised healthcare infrastructure, this article analyses qualitative interviews with 14 practitioners. Our findings reveal three key themes: (1) tensions between AI’s rule-based logic and practitioners’ human-centred approach; (2) ethical discomfort with AI performing intimate or emotionally sensitive care, especially in end-of-life contexts; (3) structural inequalities, with weak policy and infrastructure limiting effective AI integration. The study highlights that AI offers clearer benefits for routine geriatric care, such as monitoring and basic symptom management, but its utility is far more limited in the complex, relational and ethically sensitive domain of palliative care. Proposing a model of human–AI complementarity, the article argues that technology should support rather than replace the emotional and relational aspects of care and identifies policy considerations for ethically grounded integration in resource-limited contexts.

While Artificial Intelligence (AI) promises significant enhancements for Clinical Decision Support Systems (CDSSs), the opacity of many AI models remains a major barrier to clinical adoption, primarily due to interpretability and trust challenges. Explainable AI (XAI) seeks to bridge this gap by making model reasoning understandable to clinicians, but technical XAI solutions have too often failed to address real-world clinician needs, workflow integration, and usability concerns. This study synthesizes persistent challenges in applying XAI to CDSS—including mismatched explanation methods, suboptimal interface designs, and insufficient evaluation practices—and proposes a structured, user-centered framework to guide more effective and trustworthy XAI-CDSS development. Drawing on a comprehensive literature review, we detail a three-phase framework encompassing user-centered XAI method selection, interface co-design, and iterative evaluation and refinement. We demonstrate its application through a retrospective case study analysis of a published XAI-CDSS for sepsis care. Our synthesis highlights the importance of aligning XAI with clinical workflows, supporting calibrated trust, and deploying robust evaluation methodologies that capture real-world clinician–AI interaction patterns, such as negotiation. The case analysis shows how the framework can systematically identify and address user-centric gaps, leading to better workflow integration, tailored explanations, and more usable interfaces. We conclude that achieving trustworthy and clinically useful XAI-CDSS requires a fundamentally user-centered approach; our framework offers actionable guidance for creating explainable, usable, and trusted AI systems in healthcare.