Explainable Artificial Intelligence in Nuclear Medicine: Advancing Transparency in PET and SPECT Imaging and Radiation Therapy

Hossein  Arabi; Masoud  Noroozi; Hamed  Aghapanah; Sayna  Jamaati; Ali Saeeidi  Rad; Soroush  Salari; Jafar  Majidpour; Sirwan  Maroufpour; Habibollah  Dadgar; Francesca  Russo; Andrea  Cimini

doi:10.18502/fbt.v13i1.20789

Hossein Arabi Division of Nuclear Medicine & Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
Masoud Noroozi Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Hamed Aghapanah School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Sayna Jamaati Department of Energy Engineering, Sharif University of Technology, Tehran, Iran
Ali Saeeidi Rad Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Soroush Salari Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
Jafar Majidpour Department of Software Engineering, College of Engineering, University of Raparin, Ranya, Kurdistan Region, Iraq
Sirwan Maroufpour Medical Physics Group, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
Habibollah Dadgar Cancer Research Center, RAZAVI Hospital, Imam Reza International University, Mashhad, Iran
Francesca Russo Nuclear Medicine Unit, St. Salvatore Hospital, 67100 L'Aquila, Italy
Andrea Cimini Nuclear Medicine Unit, St. Salvatore Hospital, 67100 L'Aquila, Italy

DOI: https://doi.org/10.18502/fbt.v13i1.20789

Keywords: Explainable Artificial Intelligence, Artificial Intelligence, Positron Emission Tomography, Single Photon Emission Computed Tomography, Nuclear Medicine.

Abstract

The integration of Artificial Intelligence (AI) into nuclear medicine has transformed diagnostic and therapeutic processes, yet the opaque nature of many AI models hinders clinical adoption and trust. This narrative review aims to synthesize the current landscape of explainable AI (XAI) in nuclear medicine, emphasizing its role in enhancing transparency, bias mitigation, and regulatory compliance for robust clinical integration. Key chapters cover the fundamentals of XAI in nuclear medicine; XAI applications in PET and SPECT instrumentation and acquisition; image reconstruction; quantitative imaging and corrections; post-reconstruction processing and analysis; and radiotherapy. The review concludes with a discussion of challenges, limitations, and future directions, advocating for interdisciplinary advancements to bridge AI innovation with practical utility in patient care.