Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.

Background: Transthyretin amyloid cardiomyopathy (ATTR-CM) remains under-recognised, and earlier identification is increasingly important with disease-modifying therapy. Artificial intelligence (AI) applied to routine cardiovascular data may support screening, triage and diagnostic interpretation. Methods: MEDLINE, Embase, CINAHL, Web of Science and CENTRAL were searched from inception to August 2026 for studies evaluating AI/machine learning for ATTR-CM detection, screening or classification, including broader cardiac-amyloidosis models with separately extractable ATTR-specific performance and models detecting imaging phenotypes relevant to ATTR-CM screening. Risk of bias was assessed using an adapted QUADAS-2 framework, with AI-specific considerations informed by QUADAS-AI development work; selected TRIPOD + AI and CLAIM domains were assessed descriptively. Owing to substantial clinical and methodological heterogeneity, meta-analysis was not performed. Results: Twenty-eight primary reports were included, representing > 120,000 report-level participant entries, although overlapping cohorts precluded estimation of a unique-patient total. Performance varied by modality and task. AUCs ranged approximately 0.55-0.97 for ECG-containing models, 0.72-1.00 for echocardiography/POCUS and 0.70-0.85 for CT-based single-modality approaches; CMR differentiation of ATTR from AL achieved an AUC of 0.92. Nuclear-imaging models often showed high discrimination, including external-testing AUCs of 0.925-1.000, although several targeted tracer-uptake phenotypes rather than definitive ATTR-CM. Recurrent concerns included enriched populations, limited event counts, internal-only testing, threshold optimisation and incomplete calibration reporting. Conclusions: AI shows promise for ATTR-CM screening and automated interpretation, but reported discrimination should not be equated with real-world clinical performance. Prospective multicentre validation, calibration at realistic prevalence, transparent reporting and workflow-level evaluation are required before routine implementation.