A Dataset for Understanding Radiologist-Artificial Intelligence Collaboration

• Published in: Scientific data Vol. 12; no. 1; pp. 739 - 7
• Main Authors: Moehring, Alex, Kutwal, Manasi, Huang, Ray, Banerjee, Oishi, Jacobi, Adam, Eber, Corey, Mendoza, Dexter, Chung, Mike, Dayan, Etan, Gupta, Yogesh, Bui, Tan D. T., Truong, Steven Q. H., Pareek, Anuj, Langlotz, Curtis P., Lungren, Matthew P., Agarwal, Nikhil, Rajpurkar, Pranav, Salz, Tobias
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/700/139; 692/700/1421/1770; 692/700/3934; 706/689/159; 706/689/2788; Artificial Intelligence; Collaboration; Cooperative Behavior; Data Descriptor; Datasets; Decision making; Humanities and Social Sciences; Humans; Medical imaging; multidisciplinary; Radiography, Thoracic; Radiologists; Radiology; Science; Science (multidisciplinary); Thorax
• DOI: 10.1038/s41597-025-05054-0
• ISSN: 2052-4463

This dataset, Collab-CXR, provides a unique resource to study human-AI collaboration in chest X-ray interpretation. We present experimentally generated data from 227 professional radiologists who assessed 324 historical cases under varying information conditions: with and without AI assistance, and with and without clinical history. Using a custom-designed interface, we collected probabilistic assessments for 104 thoracic pathologies using a comprehensive hierarchical reporting structure. This dataset is the largest known comparison of human-AI collaborative performance to either AI or humans alone in radiology, offering assessments across an extensive range of pathologies with rich metadata on radiologist characteristics and decision-making processes. Multiple experimental designs enable both within-subject and between-subject analyses. Researchers can leverage this dataset to investigate how radiologists incorporate AI assistance, factors influencing collaborative effectiveness, and impacts on diagnostic accuracy, speed, and confidence across different cases and pathologies. By enabling rigorous study of human-AI integration in clinical workflows, this dataset can inform AI tool development, implementation strategies, and ultimately improve patient care through optimized collaboration in medical imaging.