ReviewCurrent heart failure reports2026
Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.
Review in Current heart failure reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThis review synthesizes recent progress in applications of artificial intelligence to heart failure care, including phenotyping, risk stratification, imaging interpretation, and point-of-care decision support, and delineates barriers that currently limit safe and equitable clinical translation. RECENT
findingsClinical datasets remain heterogeneous and incomplete; fragmentation across electronic records, telemetry, and imaging repositories constrains generalizability and external validity. Underrepresentation of key subgroups and outcome misclassification introduce systematic error that can widen disparities. Performance drifts as therapies and workflows evolve, yet monitoring after deployment is uncommon. Model opacity hinders error analysis and clinician trust. Regulatory and data-sharing frameworks are evolving and inconsistent, complicating multisite validation and ongoing surveillance. Mitigation strategies with the strongest support include rigorous cohort curation; transparent reporting; geographic and temporal external validation; prospective pilots with prespecified safety checks; bias auditing with equity metrics; concise documentation such as model cards and factsheets; continuous monitoring with clear contingency and rollback plans; and human oversight embedded throughout governance. Embedding safeguards into development and implementation can enable AI to deliver measurable value in heart failure care while protecting patient safety and equity. Immediate priorities are robust evaluation, routine surveillance for drift and harm, and alignment with outcomes that matter to patients.
Indexed as
Identifiers
42295460What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.