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
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.
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| Files | ||
| Issue | Vol 13 No 1 (2026) | |
| Section | Literature (Narrative) Review(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i1.20789 | |
| Keywords | ||
| XAI Artificial Intelligence PET SPECT Nuclear Medicine | ||
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How to Cite
1.
Arabi H, Noroozi M, Aghapanah H, Jamaati S, Saeeidi Rad A, Salari S, Majidpour J, Maroufpour S, Dadgar H, Russo F, Cimini A. Explainable Artificial Intelligence in Nuclear Medicine: Advancing Transparency in PET and SPECT Imaging and Radiation Therapy. Frontiers Biomed Technol. 2026;13(1):232-254.
