Toward Data-Driven Health Transformation: Accessibility, Interpretability, and Institutional Readiness for AI
DOI:
https://doi.org/10.61978/digitus.v2i2.833Keywords:
Artificial Intelligence in Healthcare, Big Data Analytics, Smart Hospitals, Accessibility and Equity, Healthcare Policy, Predictive Analytics, Digital Health TransformationAbstract
Artificial intelligence (AI) and big data analytics are increasingly recognized as vital tools in transforming healthcare delivery, particularly within hospital settings. This narrative review aims to explore the challenges and opportunities associated with the implementation of these technologies in urban healthcare systems. Using literature obtained from Scopus, PubMed, and Google Scholar, the review employs keywords such as "AI in healthcare," "big data analytics," and "predictive analytics in medicine" to synthesize peer-reviewed studies that examine both theoretical and practical dimensions of AI adoption. The analysis reveals that while developed countries are more equipped with infrastructure and training, developing nations often face systemic challenges such as limited funding, inadequate technology, and insufficient regulatory support. Accessibility remains a key concern, with disparities in technological adoption driven by geographic, demographic, and institutional factors. Furthermore, the review identifies gaps in the interpretability and integration of AI tools, especially in infection management and clinical decision-making. The discussion emphasizes the need for adaptive policy interventions, targeted investments in healthcare training, and the development of transparent AI systems. The study also recommends enhancing cross-sector collaboration to build scalable and inclusive health innovations. In conclusion, addressing the structural, ethical, and educational dimensions of AI deployment is essential for realizing its full potential in global healthcare improvement.
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