Erik has studied the use of early health technology assessment in the radiology AI development to facilitate value-based AI and thereby bridge the gap between research and clinical practice. and his considerable contribution were outlined in the paper, “ESR Essentials: how to get to valuable radiology AI: the role of early health technology assessment—practice recommendations by the European Society of Medical Imaging Informatics”
New and better-performing AI tools for radiology appear almost every day. However, it is questionable whether these tools address todays clinical needs. Early assessment during the development of new AI for radiology helps developers create more value for their tool while providing end-users with greater transparency on the risks and benefits of the AI.
In this work, they (Erik with the co-authors, in particular, Martijn from the BIGR) describe the concept of early health technology assessment and outline the changes needed to assess AI for radiology. Practical methods and examples of performed early health technology assessments are provided, showing how it can be applied to AI in radiology.
💡 Key takeaways:
- Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.
- Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.
- Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.
Read a related post here and the full open-access article here.