Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 13 1 2026 01 20 232 254 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, University of Raparin, Rania, 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 2025 12 03 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. https://fbt.tums.ac.ir/index.php/fbt/article/view/1534 https://fbt.tums.ac.ir/index.php/fbt/article/download/1534/549