**Author Listing - Lead/ Submitting Author (we will be communicating with this individual): Name, Institution, City and State where the work was done. Co-authors will be added in a separate section.**
Martha Grogan, Mayo Clinic, Rochester MN,  grogan.martha@mayo.edu

**Main Body of the Abstract**
Background: Non-specific 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.
Methods: A narrative review of published literature describing (1) the performance of AI tools that use data from electrocardiograms (ECG) and echocardiography (ECHO) to improve identification of CA and (2) challenges that hinder adoption of these tools.
Results: Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2,451 patients. Four studies used ECG data, eight used ECHO data and one used both. Most patients were males over age 60. 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 four studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16%-100%, and specificity from 75%-100%. Only one study reported model performance across strata of gender, age, race, and CA type. Persistent challenges to AI adoption include knowledge, trust, usability, cost, EHR/IT interoperability, patient-related factors, workflow, and strategic partnerships (see table)
Conclusions: 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 should be widely adopted.



**Author Listing - Lead/ Submitting Author (we will be communicating with this individual): Name, Institution, City and State where the work was done. Co-authors will be added in a separate section.**
Martha Grogan, Mayo Clinic, Rochester MN,  grogan.martha@mayo.edu

**Main Body of the Abstract**
**Background:** Non-specific 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.
**Methods:** A narrative review of published literature describing (1) the performance of AI tools that use data from electrocardiograms (ECG) and echocardiography (ECHO) to improve identification of CA and (2) challenges that hinder adoption of these tools.
**Results:** Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2,451 patients. Four studies used ECG data, eight used ECHO data and one used both. Most patients were males over age 60. 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 four studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16%-100%, and specificity from 75%-100%. Only one study reported model performance across strata of gender, age, race, and CA type. Persistent challenges to AI adoption include knowledge, trust, usability, cost, EHR/IT interoperability, patient-related factors, workflow, and strategic partnerships (see table)
**Conclusions:** 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 should be widely adopted.



The value of AI for enhancing suspicion of cardiac amyloidosis using electrocardiography and echocardiography: A narrative review PA12 (#516)

We invite you to attend the XIX International Symposium on Amyloidosis to be held in Rochester, MN.

This symposium is organized as a hybrid meeting (with the focus on in-person) held from Sunday, May 26 through Thursday, May 30, 2024.

Together with the ISA Board and members of Mayo Clinic, a very attractive scientific hybrid program is being planned. The program will cover a variety of types of systemic amyloidosis: ATTR (hereditary and wild-type), AL (light-chain), AA and less frequent forms. There will be key lectures, specific symposia including those company sponsored, oral presentations and poster sessions of accepted abstracts.

COURSE DESCRIPTION
This program will provide a comprehensive review of state-of-the-art education in systemic amyloidosis: basic science, pathophysiology, diagnosis, current and emerging treatments. Scientific presentations will include competitive oral and poster abstracts. The rapid evolution of scientific discovery and expanding treatment options has created tremendous interest in the field and this meeting will fill and important gap in educating scientists and clinicians.