Published March 4, 2021 | Version Published
Journal article Open

AI in Medical Imaging Informatics: Current Challenges and Future Directions

  • 1. Department of Computer Science, University of Cyprus, Nicosia, Cyprus
  • 2. Electrical and Computer Engineering Department, University of Louisville, Louisville, USA
  • 3. University of Kragujevac, Kragujevac, Serbia
  • 4. Emory University Atlanta, USA
  • 5. School of Engineering, The University of Edinburgh, U.K. The Alan Turing Institute, U.K.
  • 6. Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
  • 7. Department of Pathology and Laboratory Medicine, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, USA
  • 8. U.S. Department of Veterans Affairs Boston Healthcare System, Boston, USA
  • 9. Medical School, National and Kapodistrian University of Athens, Athens, Greece
  • 10. Stony Brook University, Stony Brook, USA
  • 11. School of Medicine, Regenstrief Institute, Indiana University, USA
  • 12. Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
  • 13. Technical University of Crete, Chania, Greece
  • 14. Department of Computer Science of the University of Cyprus, Nicosia, Cyprus Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE CoE), Nicosia, Cyprus


This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.


This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.


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RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission