@article {Bradshawjnumed.121.262567, author = {Tyler J. Bradshaw and Ronald Boellaard and Joyita Dutta and Abhinav K. Jha and Paul Jacobs and Quanzheng Li and Chi Liu and Arkadiusz Sitek and Babak Saboury and Peter J.H. Scott and Piotr J. Slomka and John J. Sunderland and Richard L. Wahl and Fereshteh Yousefirizi and Sven Zuehlsdorff and Arman Rahmim and Ir{\`e}ne Buvat}, title = {Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development}, elocation-id = {jnumed.121.262567}, year = {2021}, doi = {10.2967/jnumed.121.262567}, publisher = {Society of Nuclear Medicine}, abstract = {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.}, issn = {0161-5505}, URL = {https://jnm.snmjournals.org/content/early/2021/11/04/jnumed.121.262567}, eprint = {https://jnm.snmjournals.org/content/early/2021/11/04/jnumed.121.262567.full.pdf}, journal = {Journal of Nuclear Medicine} }