PT - JOURNAL ARTICLE AU - Tyler J. Bradshaw AU - Ronald Boellaard AU - Joyita Dutta AU - Abhinav K. Jha AU - Paul Jacobs AU - Quanzheng Li AU - Chi Liu AU - Arkadiusz Sitek AU - Babak Saboury AU - Peter J.H. Scott AU - Piotr J. Slomka AU - John J. Sunderland AU - Richard L. Wahl AU - Fereshteh Yousefirizi AU - Sven Zuehlsdorff AU - Arman Rahmim AU - Irène Buvat TI - Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development AID - 10.2967/jnumed.121.262567 DP - 2021 Nov 01 TA - Journal of Nuclear Medicine PG - jnumed.121.262567 4099 - http://jnm.snmjournals.org/content/early/2021/11/04/jnumed.121.262567.short 4100 - http://jnm.snmjournals.org/content/early/2021/11/04/jnumed.121.262567.full AB - The nuclear medicine field has seen a rapid expansion of academic and commercial interests 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 for technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations followed by descriptions on 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.