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

期刊: Cardiology in the young 日期: 2026-01-01 PMID: 41944643 DOI: 10.1017/S1047951126111950 浏览: 4
作者: Kutty Shelby
Shelby, K. (2026). The road to innovation: Collaborative enhancement of clinical prediction-A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture.. Cardiology in the young. https://doi.org/10.1017/S1047951126111950
Shelby K. The road to innovation: Collaborative enhancement of clinical prediction-A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture.. Cardiology in the young. 2026; doi: 10.1017/S1047951126111950
Shelby K. The road to innovation: Collaborative enhancement of clinical prediction-A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture.[J]. Cardiology in the young. 2026. DOI: 10.1017/S1047951126111950.
@article{shelby2026,
  author = {Kutty Shelby},
  title = {The road to innovation: Collaborative enhancement of clinical prediction-A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture.},
  journal = {Cardiology in the young},
  year = {2026},
  doi = {10.1017/S1047951126111950},
  note = {PMID: 41944643},
}
TY  - JOUR
AU  - Kutty Shelby
TI  - The road to innovation: Collaborative enhancement of clinical prediction-A 9th World Congress of Pediatric Cardiology and Cardiac Surgery closing plenary lecture.
T2  - Cardiology in the young
PY  - 2026
DO  - 10.1017/S1047951126111950
AN  - PMID:41944643
ER  - 

摘要

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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