智能科学创新：通过人工智能与数据科学加强高等教育和医学的综合数字化转型模型

Abstract

The rapid evolution of artificial intelligence (AI), data science, and digital platforms has driven a profound transformation in how educational institutions, research centers, and healthcare organizations generate, manage, and utilize knowledge.

In this context, the convergence of intelligent technologies and interdisciplinary processes offers an opportunity to strengthen scientific innovation, optimize decision-making, and enhance institutional efficiency through the automated analysis of large volumes of information. Various international bodies have highlighted digital transformation as a key driver of sustainable development and knowledge generation during the current decade (Organisation for Economic Co-operation and Development [OECD], 2021; UNESCO, 2023).

The objective of this research was to design and evaluate a conceptual framework known as the Smart Knowledge Integration Model (SKIM), aimed at integrating AI, data science, and digital platforms to bolster the digital transformation of higher education and the healthcare sector through interoperable architectures. To this end, an applied study with a quantitative approach was conducted using a simulated dataset comprising 2,500 academic and clinical records to compare the performance of various machine learning algorithms.

The simulated results indicated that the Extreme Gradient Boosting (XGBoost) algorithm achieved the best predictive performance, reaching an accuracy of 97.1%, followed by Artificial Neural Networks (96.7%), Random Forest (96.2%), and Support Vector Machines (94.8%). Furthermore, the SKIM model enabled the representation of a digital ecosystem capable of integrating multidisciplinary information, optimizing institutional processes, and strengthening scientific knowledge generation through advanced data mining and machine learning techniques.

It is concluded that the convergence of AI, data science, and digital platforms constitutes a viable strategy for fostering interdisciplinary scientific innovation and accelerating digital transformation processes in both higher education institutions and healthcare organizations. The data, methodology, and results presented correspond to a simulated experimental scenario intended for academic and publishing purposes.