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

Journal: Journal of translational medicine
PMID: 40065389

Abstract

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.

Authors

  • Laith Abualigah
    Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan.
  • Saleh Ali Alomari
    Faculty of Science and Information Technology, Jadara University, Irbid, 21110, Jordan.
  • Mohammad H Almomani
    Department of Mathematics, Facility of Science, The Hashemite University, P.O box 330127, Zarqa, 13133, Jordan.
  • Raed Abu Zitar
    Faculty of Engineering and Computing, Liwa College, Abu Dhabi, United Arab Emirates.
  • Kashif Saleem
    Department of Computer Science, College of Computer & Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
  • Hazem Migdady
    CSMIS Department, Oman College of Management and Technology, Barka, 320, Oman.
  • Václav Snášel
    Faculty of Electrical Engineering and Computer Science, Department of Computer Science, VŠB - Technical University of Ostrava, 17. listopadu 15/2172, 708 33, Ostrava - Poruba, Czech Republic. vaclav.snasel@vsb.cz.
  • Aseel Smerat
    Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan.
  • Absalom E Ezugwu
    School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa.

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