Digital transformation (DT) in healthcare has traditionally been conceptualized through models that privilege either technological or organizational perspectives. However, this study shows that DT cannot be understood as a linear or purely technical process but rather as a dynamic adjustment among policy frameworks, organizational capabilities, and technological change. To capture this complexity, we conducted a systematic and integrative review that combined bibliometric mapping and a grounded qualitative synthesis of literature published between 2010 and 2025, guided by three research questions: (RQ1) What are the key themes and trends in healthcare DT? (RQ2) What policy-level barriers and enablers shape its development? and (RQ3) What strategic challenges and benefits influence DT in healthcare institutions? Findings reveal that prevailing models—such as the HIMSS EMRAM and McKinsey 7S frameworks—offer valuable benchmarks but remain limited by static and technocentric assumptions. Building on these insights, this study proposes an integrated framework grounded in Social Systems Theory, which conceptualizes DT as an adaptive system characterized by iterative feedback, learning, and recalibration across policy, organizational, and technological domains. The framework contributes both a theoretical advancement toward an evolutionary understanding of DT and a practical roadmap for policymakers and healthcare managers aiming to build sustainable, interoperable, and human-centered health systems.
Artificial intelligence (AI) offers significant potential to accelerate drug discovery, yet its adoption remains constrained by interdependent technical, organizational, and regulatory barriers. This study applies Fuzzy DEMATEL to analyze twelve challenges and uncover their causal structure using expert judgments expressed as triangular fuzzy numbers. Following normalization and CFCS defuzzification, Resistance to Change, Lack of Standardization, Limited Trust in AI Outputs, Talent Shortage, and Lack of Clinical Validation emerge as the primary causal drivers. These upstream barriers exert strong influence on downstream effects, including Regulatory Barriers, Biological Complexity, Integration with Legacy Systems, and Poor Data Quality. The findings indicate that several constraints commonly viewed as external are, in practice, manifestations of internal organizational and technical shortcomings. By providing a structured causal prioritization of barriers, this study offers actionable guidance for targeting high-leverage interventions in AI-enabled drug discovery. Findings reflect expert judgment within a North Cyprus context and should be interpreted as indicative organizational signals rather than universal prescriptions.
During China’s medieval period (Wei 魏 to Song 宋 Dynasties), the Silk Road facilitated vibrant cultural exchanges, exemplified by Dunhuang’s Grape Harvest Worshipping Festival 敦煌結葡萄賽神節 under the Tang Dynasty’s Guiyi Army 歸義軍. This festival, focused on agricultural abundance and the ritual worship of grapes, appears to reflect distant resonances of Greek Dionysian practices, possibly transmitted via Sogdian intermediaries. Through a comprehensive study of Dunhuang manuscripts, visual iconography, and comparative ritual analysis, this paper argues that the festival represents a localized rearticulation of Dionysian symbolism within the Chinese agrarian and cosmological system. By incorporating both textual and material sources, and engaging recent scholarship on ritual hybridization, transculturation, and symbolic translation, this research situates Dunhuang’s ritual culture within a broader matrix of Silk Road exchanges.
Generative artificial intelligence (GenAI) challenges final-product-only coursework assessment: a polished report or design proposal may no longer show how students used AI, which claims they checked, how they attributed sources and AI contributions, or what they revised. This study examined traceable assessment design as a practical response. Rather than relying on AI detection or broad permission rules, assessment tasks were redesigned to collect evidence of students’ AI use, verification, attribution, feedback-mediated revision, and reflective judgement. Using a multi-cycle action research design with an embedded mixed-methods approach, we studied two professional courses: anatomy and physiology as the main evaluation setting and modern microscopy as a secondary adaptation setting. Data sources included coursework, AI-use statements, verification logs, assessment records, student interviews, teacher redesign logs, and traceability-related process indicators. In the main setting, the redesign class recorded higher cumulative report scores than the comparison class (21.59 vs 17.91/30; mean difference = 3.68, 95% CI [1.80, 5.56], Welch t(63.1) = 3.91, p < 0.001, d = 0.96). Of this difference, 3.29 points (89.4%) were concentrated in the redesign-targeted disclosure/citation, verification, and reflection dimensions, compared with 0.39 points across physiology accuracy and the data/evidence chain. The redesign class also recorded higher scores on a design-aligned ethics-focused assessment (14.53 vs 12.36/20; mean difference = 2.17, 95% CI [1.08, 3.26], Welch t(63.9) = 3.99, p < 0.001, d = 0.98), which was not independent evidence of broader ethical learning. Neither OSPE comparison was statistically significant (p = 127 and 0.334). Across implementation phases, AI-use statement uptake was 88.2% and 91.2%, and verification-log completeness was 1.44 and 1.74. In microscopy, group report scores across three technically different assignments (7.60, 8.03, and 8.60/10) were interpreted descriptively, not as longitudinal improvement. Traceable assessment thus made students’ documented GenAI use, verification, attribution, and revision more inspectable without establishing improved disciplinary learning.
Generative Artificial Intelligence (GenAI) is increasingly reshaping narrative practices through character monologues and emotionally guided storytelling on digital platforms such as Bilibili and Chinese TikTok. While existing AI ethics scholarship has primarily focused on issues of bias, transparency, and governance, it offers limited explanatory power for understanding how generative systems reorganize the interpretive conditions under which moral judgment is formed. To address this gap, this study develops Generative Interpretive Mediation (GIM) as a mechanism-based framework for analyzing how GenAI intervenes in interpretive processes prior to explicit ethical evaluation. Based on an interpretive research design, the article examines widely circulated AI-generated character monologues as analytically selected cases of platform-mediated narrative production. The analysis identifies three interrelated operations: interpretive selection, interpretive pre-structuring, and interpretive orientation. Together, these operations do not simply produce narratives, but stabilize interpretation by reducing narrative ambiguity and guiding users toward coherent psychological trajectories. In doing so, GenAI often replaces sustained interpretive effort with pre-shaped explanatory closure, thereby reshaping how understanding is formed in everyday engagement with digital narratives. By bringing narrative hermeneutics into dialogue with behavioral accounts of the “illusion of understanding” and “decisional inertia,” the article situates GIM within an emerging epistemic turn in AI ethics. It argues that the central ethical concern of GenAI lies not primarily in biased outputs, but in its capacity to reorganize interpretive environments in ways that subtly condition the formation of public moral judgment.
