Artificial intelligence (AI) promises to provide tools that will enhance the efficiency and accuracy of radiologic diagnoses. RSNA organizes AI challenges to spur the creation of AI tools for radiology.
To build these tools, AI researchers need access to substantial volumes of imaging data annotated by expert radiologists. Data challenges engage the radiology community to develop such datasets, which provide the standard of truth in training AI systems to perform tasks relevant to diagnostic imaging.
In a challenge, researchers compete on how well their AI models perform specific tasks such as detection, localization and categorization of abnormal features according to defined performance measures. Each AI challenge explores and demonstrates the ways AI can benefit radiology and improve patient care.
These AI data challenges are organized by the RSNA Radiology Informatics Committee, often in collaboration with other radiological organizations from around the world. Please direct questions about the AI data challenge program to firstname.lastname@example.org.
How does an AI challenge work?
There are two main phases of an AI challenge: training and evaluation.
In the training phase, researchers develop models and run them against the labeled data to get feedback on how closely their results match the expert annotations. In the evaluation phase, models are evaluated and scored against a portion of the dataset without labels. Winners are determined based on their scores in this phase.
Past AI challenges
View information about past challenges here:
2021: COVID-19 AI Detection Challenge
2021: Brain Tumor AI Challenge
2020: RSNA Pulmonary Embolism Detection Challenge
2019: RSNA Intracranial Hemorrhage Detection Challenge
2018: RSNA Pneumonia Detection Challenge
2017: RSNA Pediatric Bone Age Challenge