Abstract
The integration of Artificial Intelligence (AI) in radiology, particularly through Computer-Aided Detection (CAD) systems, promises significant advancements in diagnostic accuracy, workflow optimization, and educational outcomes. Despite the potential benefits, challenges such as professional deskilling, overreliance on AI, and regulatory issues are still feared. This study aims to evaluate the practical, educational, and personal implications of introducing a CAD AI-tool in radiology residency. Through a mixed-methods approach involving ten radiology residents, we want to assess the impact of AI on diagnostics, bias, workflow, and resident-supervisor interactions. In the study, we monitor and interview residents that are analyzing chest X-rays using the CAD system. Preliminary findings from pilot studies indicate a generally positive reception but highlight the need for improvements. This research seeks to provide insights into the responsible deployment of AI in radiology training.
Original language | English |
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Title of host publication | NordiCHI '24 Adjunct : Adjunct Proceedings of the 13th Nordic Conference on Human-Computer Interaction, NordiCHI 2024 |
Publisher | ACM - Association for Computing Machinery |
Publication date | 13.10.2024 |
Article number | 22 |
ISBN (Electronic) | 979-8-4007-0965-4 |
DOIs | |
Publication status | Published - 13.10.2024 |
MoE publication type | A4 Article in conference proceedings |
Event | 13th Nordic Conference on Human-Computer Interaction, NordiCHI 2024 - Uppsala, Sweden Duration: 13.10.2024 → 16.10.2024 Conference number: 13 https://dl.acm.org/doi/proceedings/10.1145/3677045 |
Publication series
Name | ACM International Conference Proceeding Series |
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Keywords
- 113 Computer and information sciences
- Artificial Intelligence
- Computer-Aided Detection
- Human-AI Collaboration
- Radiology
- Resident Training