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Implementing Artificial Intelligence in Radiology: Design Thinking Road Map
Vitor Ulisses Monnaka1, Jéssica Andrade-Silva1, Gilberto Szarf2
1Department of Innovation, Hospital Israelita Albert Einstein, Avenida Albert Einstein, 627/701, São Paulo, 05652-900, Brazil, 55 1121511233.
JMIR AI
|April 29, 2026
Summary
Design thinking offers a roadmap for integrating artificial intelligence (AI) in radiology. This user-centered approach addresses challenges like performance and security for better clinical AI deployment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Clinical Implementation
Background:
- Artificial intelligence (AI) shows great promise for medical imaging but faces significant challenges in clinical practice.
- Real-world AI integration is hindered by limited clinical impact, performance issues, security vulnerabilities, and regulatory hurdles.
Purpose of the Study:
- To explore how design thinking principles can guide AI implementation in radiology.
- To provide a structured approach for overcoming barriers to AI adoption in clinical settings.
Main Methods:
- Applying design thinking principles: user-centeredness, multidisciplinary collaboration, and iterative refinement.
- Focusing on identifying clinical needs, selecting validated AI solutions, and ensuring effective deployment.
Main Results:
- Design thinking provides a practical framework for AI implementation in radiology.
- This approach facilitates the identification of needs, validation of solutions, and continuous improvement.
Conclusions:
- Design thinking offers a structured roadmap for successful AI integration in radiology.
- Emphasizing user needs and collaboration is key to overcoming implementation challenges and ensuring effective AI deployment.
