The goal of this prospective, multicenter, open-label, blinded end-point pragmatic study is to evaluate an artificial intelligence (AI)-augmented echocardiography screening approach for early detection of metabolic dysfunction associated steatotic liver disease (MASLD) and/or cirrhosis, in patients undergoing routine transthoracic echocardiograms (TTEs).

The main question it aims to answer is to:

1. Evaluate notification responsiveness and rates of confirmatory testing for patients identified as high risk for having liver disease to determine whether optimized notifications increase timely confirmatory testing and treatment initiation versus standard of care assessment.
2. Compare time to diagnosis, treatment uptake, and clinical outcomes (hospitalizations, incident ASCVD, mortality) between cohorts identified as high risk by the AI algorithm and comparison groups to determine whether AI guided screening shortens time to diagnosis and increases appropriate treatment.

Inclusion Criteria:

• Adults ≥18 years.
• Underwent routine TTE within site defined recent timeframe and flagged as high risk for MASLD and/or cirrhosis by the AI model using pre specified threshold.
• Able to provide informed consent; reachable for follow up.

Exclusion Criteria:

• Inability to consent or communicate.
• Enrollment in hospice or life expectancy so limited that additional evaluation would not be appropriate per clinician judgment.
• Clinical circumstances where immediate alternative diagnostic pathways supersede study procedures (e.g., acute decompensation requiring urgent management).
• Prior liver or kidney transplant.
• Patient unwilling to undergo prospective testing for liver disease.