Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness

Clinical Snapshot

Key Findings

• P Value: Not specified in this narrative review

• Effect Size: Reproducibility approaching or exceeding trained human readers in select settings

• Primary Outcome: AI-assisted ultrasound demonstrates formal equivalence to expert readers for left ventricular ejection fraction measurement

• Nnt Or Sensitivity: High rate of diagnostic acceptability achieved by non-expert operators using AI guidance

• Confidence Interval: Not specified in this narrative review

Clinical Application

Increasingly feasible for decentralized trials with appropriate regulatory oversight and algorithm validation Relevant to TGA medical device regulations and potential integration with Australian clinical trial infrastructure, though specific Australian regulatory guidance not addressed Clinical trial participants requiring ultrasound-based endpoint assessment, particularly in cardiology and pulmonology studies

Abstract

Ultrasound is among the most widely used imaging modalities in clinical trials, and yet its dependence on operator skill and equipment settings has historically limited the reproducibility of ultrasound-based endpoints in multi-center studies. Artificial intelligence (AI) now addresses this limitation across two complementary dimensions: automated measurement algorithms that quantify cardiac function, organ volume, and vascular parameters with reproducibility approaching or, in select settings, exceeding that of trained human readers and real-time acquisition guidance systems that enable clinicians with no formal sonography training to perform diagnostic-level examinations, making remote and decentralized assessment increasingly feasible. This narrative review synthesizes current evidence and regulatory developments across three interconnected domains. First, use of automated ultrasound to ascertain endpoints has advanced from single-institution validation to prospective and randomized evidence, with deep learning measurement of the left ventricular ejection fraction demonstrating formal equivalence to expert readers across multiple echocardiographic parameters and AI-first workflows shortening the time to diagnosis in a blinded non-inferiority trial. Second, AI-guided ultrasound acquisition by nurses and other non-expert operators has achieved a high rate of diagnostic acceptability in cardiac and pulmonary ultrasound, laying the groundwork for use of ultrasound-based endpoints in decentralized clinical trial designs, as reflected in Food and Drug Administration (FDA) guidance on decentralized trial elements. Third, the regulatory frameworks governing AI-enabled medical devices - including the U.S. FDA's Predetermined Change Control Plan guidance, the EU Artificial Intelligence Act, and internationally harmonized good machine learning practice relevant to Japan and other jurisdictions - increasingly emphasize overlapping principles such as specification of prospective performance, post-marketing oversight, and transparent reporting. Addressing remaining challenges in domain generalization across vendors, subgroup fairness, and algorithm change management during ongoing trials will be essential for AI-assisted ultrasound to fulfill its potential as a robust, scalable endpoint in clinical research worldwide.

References

1. 1.Karako, K., & Gao, J. (2026). Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness. Drug Discoveries & Therapeutics. https://doi.org/10.5582/ddt.2026.01020