Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development

• Published in: Journal of Nuclear Medicine Vol. 63; no. 4; pp. 500 - 510
• Main Authors: Bradshaw, Tyler J., Boellaard, Ronald, Dutta, Joyita, Jha, Abhinav K., Jacobs, Paul, Li, Quanzheng, Liu, Chi, Sitek, Arkadiusz, Saboury, Babak, Scott, Peter J.H., Slomka, Piotr J., Sunderland, John J., Wahl, Richard L., Yousefirizi, Fereshteh, Zuehlsdorff, Sven, Rahmim, Arman, Buvat, Irène
• Format: Journal Article
• Language: English
• Published: United States Society of Nuclear Medicine 01.04.2022
• Subjects: Algorithms; Artificial Intelligence; Best practice; Life Sciences; Molecular Imaging; Nuclear Medicine; Radionuclide Imaging; The State of The Art; research methods; computer/PACS; artificial intelligence; best practices; algorithm; statistics; Deep learning; Machine learning; Research Methods; Artificial intelligence; Statistics; Algorithm; Best practices; Computer/PACS
• ISSN: 0161-5505; 1535-5667; 2159-662X; 2159-662X; 1535-5667
• DOI: 10.2967/jnumed.121.262567

The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.