Application of Artificial Intelligence (AI) tools has recently gained interest in the fields of medical imaging and radiotherapy. Even though there have been many papers published in these domains in the last few years, clinical assessment of the proposed AI methods is limited due to the lack of standardized protocols that can be used to validate the performance of the developed tools. Moreover, each stakeholder uses their own methods, tools, and evaluation criteria. Communication between different stakeholders is limited or absent, which makes it hard to easily exchange models between different clinics. These issues are not limited to radiotherapy but exist in every AI application domain. To deal with these issues, methods like the Machine Learning Canvas, Datasets for Datasheets, and Model cards have been developed. They aim to provide information of the whole creation pipeline of AI solutions, of the datasets used to develop AI, along with their biases, as well as to facilitate easier collaboration/communication between different stakeholders and facilitate the clinical introduction of AI. This work introduces the concepts of these 3 open-source solutions including the author's experiences applying them to AI applications for radiotherapy.

© 2022 The Author(s). Published by Elsevier Inc.