BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. METHODS: This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. RESULTS: The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. CONCLUSIONS: The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Computer Science Department Al Al Bayt University Mafraq 25113 Jordan

CSMIS Department Oman College of Management and Technology 320 Barka Oman

Department of Computer Science and Engineering College of Applied Studies and Community Service King Saud University 11362 Riyadh Saudi Arabia

Department of Mathematics Facility of Science The Hashemite University P O box 330127 Zarqa 13133 Jordan

Faculty of Educational Sciences Al Ahliyya Amman University Amman 19328 Jordan

Faculty of Electrical Engineering and Computer Science VŠB Technical University of Ostrava 70800 Poruba Ostrava Czech Republic

Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates

Faculty of Science and Information Technology Jadara University Irbid 21110 Jordan

Unit for Data Science and Computing North West University 11 Hofman Street Potchefstroom 2520 South Africa

J Transl Med. 2025 Aug 14;23(1):908. doi: 10.1186/s12967-025-06948-8. PubMed

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Correction: Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care