Framework for Assessing AI in Health Technologies
Author Information
Author(s): Di Bidino Rossella, Daugbjerg Signe, Papavero Sara C., Haraldsen Ira H., Cicchetti Americo, Sacchini Dario
Primary Institution: Graduate School of Health Economics and Management, Universita Cattolica del SacroCuore
Hypothesis
Is there a standardized framework for evaluating artificial intelligence-based health technologies?
Conclusion
A robust assessment framework for AI tools in healthcare is essential for effective evaluation and decision-making.
Supporting Evidence
- 48 out of 65 proposed topics were deemed critical for inclusion in the HTA framework.
- Top topics included accuracy of the AI model and patient safety.
- Experts emphasized the need for an adaptable framework to assess diverse AI technologies.
Takeaway
Experts agree that we need a better way to evaluate AI technologies in healthcare, focusing on important topics like safety and accuracy.
Methodology
A two-round Delphi survey was conducted with 46 experts to identify critical topics for an HTA framework for AI technologies.
Potential Biases
Varying levels of expertise among panel members may have influenced the results.
Limitations
The panel lacked diverse representation, particularly from patients, and some topics lacked clear definitions.
Participant Demographics
The majority of panelists were from Europe, with a mix of clinicians, HTA experts, and technical experts.
Digital Object Identifier (DOI)
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