Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.

Journal: European journal of medical research
Published Date:

Abstract

The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized healthcare, particularly in the domains of disease prediction and diagnosis. This study provides a comprehensive review of ML and DL applications across sixteen diverse diseases, synthesizing findings from research conducted between 2015 and 2024. We explore these technologies' methodologies, effectiveness, and clinical outcomes, highlighting their transformative potential in healthcare settings. Although ML and DL demonstrate remarkable accuracy and efficiency in disease prediction and diagnosis, challenges including quality of data, interpretability of models, and their integration into clinical workflows remain significant barriers. By evaluating advanced approaches and their outcomes, this review not only underscores the current capabilities of ML and DL but also identifies key areas for future research. Ultimately, this work aims to serve as a roadmap for advancing healthcare practices, enhancing clinical decision making, and strengthening patient outcomes through the effective and responsible implementation of AI-driven technologies.

Authors

  • Hossein Sadr
    Department of Computer Engineering, Rahbord Shomal Institute of Higher Education, Rasht, Iran.
  • Mojdeh Nazari
    Cardiovascular Disease Research Center, Department of Cardiology, School of Medicine, Heshmat Hospital, Guilan University of Medical Sciences, Rasht, Iran. mojdeh.nazari@sbmu.ac.ir.
  • Zeinab Khodaverdian
    Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Ramyar Farzan
    Department of Plastic & Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
  • Shahrokh Yousefzadeh-Chabok
    Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran.
  • Mohammad Taghi Ashoobi
    Department of Surgery, School of Medicine, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran.
  • Hossein Hemmati
    Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran.
  • Amirreza Hendi
    Dental Sciences Research Center, Department of Prosthodontics, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.
  • Ali Ashraf
    Clinical Research Development Unit of Poursina Hospital, Guilan University of Medical Sciences, Rasht, Iran.
  • Mir Mohsen Pedram
    Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran.
  • Meysam Hasannejad-Bibalan
    Department of Microbiology, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
  • Mohammad Reza Yamaghani
    Department of Computer Engineering and Information Technology, La.C., Islamic Azad University, Lahijan, Iran.