NCT07153783

Interventional AI-Human Collaboration for Liver Tumor Diagnosis

AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 10,333 (actual)
• Sponsor: Shengjing Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.

Detailed description

This study aims to evaluate the effectiveness of AI-human collaboration in liver tumor diagnosis by embedding real-time AI analysis into conventional multiphasic contrast-enhanced CT (CE-CT) workflows. Specifically, this prospective validation trial will assess diagnostic performance in detecting and characterizing hepatic lesions, particularly malignancies, evaluate the feasibility and efficiency of workflow integration, and determine the potential clinical impact on treatment decision-making and patient management.

Conditions

• Hepatocellular Carcinoma (HCC)
• Intrahepatic Cholangiocarcinoma (Icc)
• Hepatic Metastasis
• Hepatic Hemangioma
• Cyst
• Focal Nodular Hyperplasia

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	AI-human collaboration for CE-CTs diagnosis	The system automatically processes all eligible same-day scans and generates results for review the following day. To maintain efficient AI-human collaboration while preserving the standard clinical workflow, the conventional radiological interpretation process remains unchanged (first-line radiologists provide initial reports followed by senior radiologists' review). A dedicated senior radiologist then evaluates any discordances between AI findings and primary radiological report. For complex cases, the review process escalates to a consensus review panel (i.e., pre-designated senior radiologists, Multidisciplinary Team (MDT)). The MDT can recommend clinical interventions including follow-up (e.g., additional imaging examinations, active surveillance), surgical procedures, or adjustments to adjuvant therapy (initiation or modification of treatment regimens). All discordant cases and their outcomes are systematically documented for longitudinal tracking and follow-up analysis.

Timeline

• Start date: 2025-09-01
• Primary completion: 2025-10-29
• Completion: 2025-11-07
• First posted: 2025-09-04
• Last updated: 2025-11-18

Locations

1 site across 1 country: China