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

AI in medical physics: guidelines for publication

Issam El Naqa  1 , John M Boone  2 , Stanley H Benedict  3 , Mitchell M Goodsitt  4 , Heang-Ping Chan  4 , Karen Drukker  5 , Lubomir Hadjiiski  4 , Dan Ruan  6 , Berkman Sahiner  7
Affiliations
Editorial

AI in medical physics: guidelines for publication

Issam El Naqa et al. Med Phys. 2021 Sep.

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.

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References

REFERENCES

    1. El Naqa I, Das S. The role of machine and deep learning in modern medical physics. Med Phys. 2020;47(5):e125-e126.
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    1. Cruz Rivera S, Liu X, Chan A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. The Lancet Digital Health. 2020;2(10):e549-e560.
    1. Sounderajah V, Ashrafian H, Aggarwal R, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med. 2020;26(6):807-808.

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