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Cited by 26Amyloidosis: Diagnosis, Treatment, OutcomesArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review

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Grogan, Martha, Lopez‐Jimenez, Francisco, Guthrie, Spencer, Kumar, Nisith, Langevin, Reuben, Lousada, Isabelle, Witteles, Ronald, Royyuru, Ajay +3 more·2025·Journal of the American Heart Association
DOI 10.1161/jaha.124.036533PMC PMC12132858
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Abstract

Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of CA and challenges that hinder adoption of these tools. Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2451 patients. Four studies used ECG data, 8 used echocardiography data, and 1 used both. The CA gold standard was typically defined as a CA diagnosis in an institutional or other database but the requirements for these diagnoses were heterogenous across studies, and many did not distinguish among CA subtypes. AI model development varied considerably, and only 4 studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 study reported model performance across strata of sex, age, race, and CA type. Persistent challenges to AI adoption include usability, cost, value added, electronic health record/information technology interoperability, patient‐related factors, regulation, and privacy and liability. Published studies on AI for improved identification of CA show favorable performance measures but numerous methodologic and other challenges must be addressed before these tools are more widely adopted.

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  • Composite SHD screening and rarer-disease applications such as cardiac amyloidosis remain at an earlier stage, with the literature still dominated by model development, retrospective validation, and limited prospective implementation evidence.

    “By contrast, composite SHD screening and rarer-disease applications such as cardiac amyloidosis remain at an earlier stage, with the literature still dominated by model development, retrospective validation, and limited prospective implementation evidence.”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [43]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The regulatory maturity of AI-ECG for SHD screening remains uneven.

    “The regulatory maturity of AI-ECG for SHD screening remains uneven.”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [43]

    In “Methodological Challenges and Future Directions” (page unavailable), the review describes different FDA statuses for cardiac AI technologies and an evolving regulatory framework, supporting the idea of uneven maturity. It discusses ECG and echocardiography tools together, however, rather than establishing the regulatory status of AI-ECG specifically across structural heart disease (SHD) screening. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • HCM and cardiac amyloidosis should be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.

    “Taken together, HCM and CA should therefore be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.26–30,43”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [43]

  • The available evidence for cardiac amyloidosis remains constrained by case-enriched designs, limited external validation, and the low-prevalence challenge intrinsic to real-world deployment.

    “However, the available evidence remains constrained by case-enriched designs, limited external validation, and the low-prevalence challenge intrinsic to real-world deployment.30,43”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [43]

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Authors
Grogan, M., Lopez‐Jimenez, F., Guthrie, S., Kumar, N., Langevin, R., Lousada, I., Witteles, R., Royyuru, A., Rosenzweig, M., Cairns‐Smith, S., & Ouyang, D.
Affiliations
  • Mayo Clinic (US)
  • Mayo Clinic
  • Attralus
  • Pfizer (United States) (US)
  • Amyloidosis Research Consortium
  • Stanford Medicine (US)
  • IBM (United States) (US)
  • City Of Hope National Medical Center (US)
  • Cedars-Sinai Medical Center (US)
Corresponding author
Grogan, Martha
ORCID
Venue
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2025-04-15
Volume
14
Issue
8
Pages
e036533-e036533
ISSN
2047-9980 (ISSN-L)
DOI
10.1161/jaha.124.036533
PMC
PMC12132858
OpenAlex
W4409338912
Cited by
26
References
2
Citations by year
2026: 21 · 2025: 5
FWCI
2.30
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of the American Heart Association (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Amyloidosis: Diagnosis, Treatment, Outcomes
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
cardiac amyloidosis, artificial intelligence, electrocardiogram, echocardiography, external validation, underdiagnosis, AI adoption barriers, electronic health records interoperability, regulatory challenges
MeSH terms
Amyloidosis, Artificial Intelligence, Echocardiography, Electrocardiography, Humans, Cardiomyopathies, Predictive Value of Tests

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