โก Quick Summary
This narrative review explores the integration of artificial intelligence (AI) in radiology, emphasizing the importance of human-AI collaboration in clinical workflows. It identifies both the risks and opportunities presented by AI tools, aiming to enhance the performance of radiologists and improve patient outcomes.
๐ Key Details
- ๐ Focus: Human-AI interaction in radiology
- ๐งฉ Key Concepts: Diagnostic complementarity, physician-in-the-loop workflows
- โ๏ธ Integration Strategies: From acquisition to reporting and teaching
- ๐ Risks: Automation bias, algorithmic aversion, deskilling
- ๐ฑ Emerging Technologies: Vision-language models, adaptive learning loops
๐ Key Takeaways
- ๐ค Collaboration between radiologists and AI can lead to synergistic performance.
- โ ๏ธ Risks include automation bias and potential deskilling of radiologists.
- ๐ Continuous monitoring is essential to address performance drift in AI systems.
- ๐ก AI literacy is crucial for radiologists to effectively utilize AI tools.
- ๐ ๏ธ Co-design with radiology teams is vital for responsible AI implementation.
- ๐ The review highlights the need for further studies to explore human-AI collaboration.
- ๐ Adaptive learning loops represent a promising direction for future AI systems.
๐ Background
The integration of artificial intelligence into routine radiology practice is rapidly evolving. However, many studies focus on evaluating AI algorithms in isolation, neglecting the critical aspect of how these technologies interact with radiologists in real-world clinical settings. Understanding this interaction is essential for maximizing the benefits of AI while minimizing potential harms.
๐๏ธ Study
This narrative review synthesizes current knowledge on human-AI interaction in radiology, outlining various conceptual frameworks and practical implications. The authors, Kocak and Cuocolo, delve into the complexities of integrating AI tools throughout the imaging pathway, from acquisition to interpretation and reporting.
๐ Results
The review identifies several key interaction models and highlights the cognitive and professional effects of AI integration. Issues such as automation bias and algorithmic aversion are discussed, along with the potential for increased workload and burnout among radiologists, particularly trainees. The authors emphasize the importance of responsible implementation to mitigate these risks.
๐ Impact and Implications
The findings of this review have significant implications for the radiology community. By recognizing where human-AI collaboration can add value, radiologists can enhance their diagnostic capabilities and improve patient care. However, it is equally important to be aware of the potential pitfalls associated with AI integration, ensuring that the technology serves as a complement rather than a replacement for human expertise.
๐ฎ Conclusion
This review provides a comprehensive overview of the current landscape of human-AI interaction in radiology. As AI continues to evolve, it is crucial for the radiology community to engage in ongoing dialogue about the responsible implementation of these technologies. By fostering collaboration and addressing the associated risks, we can harness the full potential of AI to improve healthcare outcomes.
๐ฌ Your comments
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Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.
Abstract
Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction with radiologists in clinical workflows. This narrative review summarizes current knowledge on human-AI interaction in radiology and highlights practical risks and opportunities for clinical teams. First, simple conceptual models of human-AI collaboration are described, such as diagnostic complementarity, which explain when radiologists and AI can achieve synergistic performance exceeding that of either alone. Then, AI tool integration strategies along the imaging pathway are reviewed, from acquisition and triage to interpretation, reporting, and teaching, outlining common interaction models and physician-in-the-loop workflows. Cognitive and professional effects of AI integration are also discussed, including automation bias, algorithmic aversion, deskilling, workload management, and burnout, with specific vulnerabilities for trainees. Furthermore, key elements of responsible implementation are summarized, such as liability and oversight implications, continuous monitoring for performance drift, usable explanations, basic AI literacy, and co-design with radiology teams. Finally, emerging systems are introduced, including vision-language models and adaptive learning loops. This review aims to provide a clear and accessible overview to help the radiology community recognize where human-AI collaboration can add value, where it can cause harm, and which questions future studies must address.
Author: [‘Kocak B’, ‘Cuocolo R’]
Journal: Diagn Interv Radiol
Citation: Kocak B and Cuocolo R. Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation. Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation. 2026; (unknown volume):(unknown pages). doi: 10.4274/dir.2026.263780