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How clinical data is annotated for AI projects?

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Clinical data for AI projects is annotated using various methods to ensure accuracy and reliability. Annotation inconsistencies among experts are common due to biases and judgments, impacting model development. To address this, new annotation forms like PathNarratives are proposed, providing detailed decision-to-reason structures for better understanding. Establishing databases with detailed annotations from pathology and radiology helps in training AI models effectively. Radiologists' annotations on mammographic images are crucial for AI tool development, with factors like concordance affected by breast density. Overall, high-quality annotations are essential for training AI models in clinical settings, emphasizing the need for detailed and consistent annotation processes.

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Open access•Journal Article•DOI Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training. Caroline Bivik Stadler, Martin Lindvall, Claes Lundström, Anna Bodén, Karin Lindman, Jeronimo Frias Rose, Darren Treanor, Johan Blomma, Karin Stacke, Nicolas Pinchaud, Martin Hedlund, Filip Landgren, Mischa Woisetschläger, Daniel Forsberg - Show less +13 more 01 Feb 2021-Journal of Digital Imaging 11 Citations	Clinical data is annotated with high-quality ground truth annotations for AI projects, focusing on detection, quantification, segmentation, and classification tasks to support training across various medical imaging domains.
Open access•Posted Content•DOI The Impact of Inconsistent Human Annotations on AI driven Clinical Decision Making 15 Sep 2022	Clinical data for AI projects is annotated by domain experts, but inconsistencies arise due to expert bias. Optimal models are achieved by assessing annotation learnability and using 'learnable' datasets for consensus.
Open access•Journal Article•DOI PathNarratives: Data annotation for pathological human-AI collaborative diagnosis Heyu Zhang, Xiaomin Wu, Peixiang Huang, Wenkang Qin, Fan Wang, Juxiang Ye, Xirui Huang, Yanfang Liao, Hang Chen, Limei Guo, Xue-jun Shi, Lin Luo - Show less +11 more 26 Jan 2023-Frontiers in Medicine	Clinical data is annotated for AI projects using a new form called PathNarratives, which includes decision-to-reason data structure, narrative annotation, and multimodal tools for collaborative pathological diagnosis.
Proceedings Article•DOI How do you solve a problem like concordance? a study of radiologists’ clinical annotations for mammographic AI training Zhengqiang Jiang, Phuong Dung Trieu, Ziba Gandomkar, Seyedamir Tavakoli Taba, Melissa L. Barron, Sarah J. Lewis - Show less +5 more 03 Apr 2023	Clinical data for AI projects is annotated by radiologists marking lesion locations on mammographic images. Concordance levels vary, with higher agreement in cranio-caudal views and lower with increased breast density.
Open access•Journal Article•DOI The impact of inconsistent human annotations on AI driven clinical decision making Aneeta Sylolypavan, Derek Sleeman, Honghan Wu, Malcolm Sim - Show less +3 more 21 Feb 2023-npj digital medicine 3 Citations	Clinical data for AI projects is annotated by domain experts, but inconsistencies arise due to expert bias. Optimal models are achieved by using 'learnable' annotated datasets for determining consensus.

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