AI in medical physics: guidelines for publication

Issam El Naqa; John M Boone; Stanley H Benedict; Mitchell M Goodsitt; Heang-Ping Chan; Karen Drukker; Lubomir Hadjiiski; Dan Ruan; Berkman Sahiner

doi:10.1002/mp.15170

AI in medical physics: guidelines for publication

Med Phys. 2021 Sep;48(9):4711-4714. doi: 10.1002/mp.15170.

Authors

Issam El Naqa¹, John M Boone², Stanley H Benedict³, Mitchell M Goodsitt⁴, Heang-Ping Chan⁴, Karen Drukker⁵, Lubomir Hadjiiski⁴, Dan Ruan⁶, Berkman Sahiner⁷

Affiliations

¹ Machine Learning & Radiation Oncology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.
² Department of Radiology, University of California Davis Health, Sacramento, CA, 95817, USA.
³ Radiation Oncology, University of California Davis Health, Sacramento, CA, 95817, USA.
⁴ Department of Radiology, University Michigan, 1500 E Medical Center Dr, Ann Arbor, MI, 48109, USA.
⁵ Department of Radiology, University of Chicago, 5841 S. Maryland Ave, Chicago, IL, 60637, USA.
⁶ Radiation Oncology, University of California Los Angeles School of Medicine, 200 UCLA Medical Plaza, Los Angeles, CA, 90095, USA.
⁷ Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.

PMID: 34545957
DOI: 10.1002/mp.15170

Abstract

The Abstract is intended to provide a concise summary of the study and its scientific findings. For AI/ML applications in medical physics, a problem statement and rationale for utilizing these algorithms are necessary while highlighting the novelty of the approach. A brief numerical description of how the data are partitioned into subsets for training of the AI/ML algorithm, validation (including tuning of parameters), and independent testing of algorithm performance is required. This is to be followed by a summary of the results and statistical metrics that quantify the performance of the AI/ML algorithm.

Publication types

Editorial

MeSH terms

Algorithms*
Artificial Intelligence*
Physics