The road to innovation: Collaborative enhancement of clinical prediction—A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture

Innovation in paediatric and adult congenital cardiology increasingly depends on collaboration among academia, industry, and professional communities. From this perspective, the author argues that clinical prediction represents a natural convergence point for these stakeholders, aligning safe, personalised care with economic incentives. The author discusses emerging evidence highlighting the promise of artificial intelligence-driven prediction across various cardiovascular domains, while highlighting current limitations related to narrow scope, static design, and weak integration into clinical decision-making. Medicine-based evidence and a high-quality, inclusive data infrastructure may help address these gaps. Together, these approaches, along with stakeholders upholding their responsibilities, define a path towards predictive innovation.

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Publication Details

Journal
Cardiology in the Young
Published
2026-04-07
DOI
https://doi.org/10.1017/s1047951126111950
Primary Topic
Congenital Heart Disease Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

The road to innovation: Collaborative enhancement of clinical prediction—A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture

Shelby Kutty
Cardiology in the Young
Congenital Heart Disease Studies
article

The road to innovation: Collaborative enhancement of clinical prediction—A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture

Shelby Kutty
article en

Abstract

Innovation in paediatric and adult congenital cardiology increasingly depends on collaboration among academia, industry, and professional communities. From this perspective, the author argues that clinical prediction represents a natural convergence point for these stakeholders, aligning safe, personalised care with economic incentives. The author discusses emerging evidence highlighting the promise of artificial intelligence-driven prediction across various cardiovascular domains, while highlighting current limitations related to narrow scope, static design, and weak integration into clinical decision-making. Medicine-based evidence and a high-quality, inclusive data infrastructure may help address these gaps. Together, these approaches, along with stakeholders upholding their responsibilities, define a path towards predictive innovation.

Cardiology in the Young
BayCare Health System (US), Epidemic Intelligence Service (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Congenital Heart Disease Studies
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