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Missing infrastructure for real-world predictive AI impact
Benjamin I Perry1,2, Ben Hammond3, Dominic Oliver4
1Institute for Mental Health, University of Birmingham, Birmingham, UK b.i.perry@bham.ac.uk.
BMJ Health & Care Informatics
|June 19, 2026
Summary
Predictive artificial intelligence (AI) tools can improve chronic disease management but face organizational barriers to clinical implementation. Addressing institutional ownership and stewardship is key to translating AI innovations into routine healthcare.
Area of Science:
- Health systems research
- Medical informatics
- Artificial intelligence in healthcare
Background:
- Chronic diseases represent a growing burden on healthcare systems globally.
- Predictive artificial intelligence (AI) offers potential for earlier detection and targeted interventions.
- Despite research, few AI tools are integrated into routine clinical practice.
Purpose of the Study:
- To identify the primary barriers hindering the translation of predictive AI tools into routine healthcare.
- To propose actionable recommendations for overcoming these translational challenges.
Main Methods:
- The study presents an argument based on the analysis of translational barriers in predictive AI implementation.
- It synthesizes current challenges in the adoption of AI in healthcare settings.
- Recommendations are developed based on identified organizational hurdles.
Main Results:
- The primary barrier to predictive AI tool translation is organizational, not methodological.
- Lack of defined institutional ownership and long-term stewardship impedes progress.
- Navigating regulatory certification and governance structures presents significant challenges.
Conclusions:
- Organizational factors, including ownership and stewardship, are critical for successful AI tool implementation.
- Addressing these structural issues is essential for moving predictive AI from research to practice.
- Recommendations focus on establishing clear accountability and support structures for AI in healthcare.
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