Artificial intelligence in electrocardiogram-based prediction of heart failure: a systematic review and meta-analysis
Shunhong Zhang, Jun Jiang, Yi Luo, Guangyue Liu, Saidi Hu, Siran Wan, Chenchen Luo, Hong Li, Nian Li, LinYong Zhao · 2026
Read Artificial intelligence in electrocardiogram-based prediction of heart failure: a systematic review and meta-analysis by Shunhong Zhang, Jun Jiang, Yi Luo, Guangyue Liu, Saidi Hu, Siran Wan, Chenchen Luo, Hong Li, Nian Li, LinYong Zhao (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12808466.txt.
Description
Shunhong Zhang and co-authors pool, in a 2026 review, studies that use AI on heart tracings to predict heart failure before it strikes. Catching high-risk people early can delay or prevent the disease. A standard ECG is cheap and common, which makes it an tempting input for AI. The team searched four databases for work from 2005 to 2025 and kept studies that reported a standard accuracy score.
Five studies, with eleven cohorts and well over a million people in total, met the bar. Scores were pooled with a random-effects method, and the team probed why results varied, including checks across ethnic groups and a formal bias tool. The review gives readers a sober read on how well a plain heart tracing, read by AI, can flag future failure.
Catalog record
| CREATOR | Shunhong Zhang, Jun Jiang, Yi Luo, Guangyue Liu, Saidi Hu, Siran Wan, Chenchen Luo, Hong Li, Nian Li, LinYong Zhao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 28 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |