From:  Artificial intelligence in the interventional management of liver disease: a narrative review from foundational concepts to clinical applications

 Key challenges and future directions for AI in liver interventions.

CategoryKey pointsDescriptionReference(s)
ChallengesData-related hurdlesScarcity of large, high-quality, and standardized IR datasets.
“Garbage in, garbage out”: poor image quality leads to AI failure.
Ethical issues surrounding data privacy, ownership, and security.		[19, 23, 36, 59, 60]
Methodological barriersThe “black box” problem and the need for explainable AI (XAI).
Lack of external validation, leading to overfitting.
High heterogeneity across studies makes comparing results difficult.		[1, 20, 23, 28, 37, 38, 61, 62]
Clinical & ethical dilemmasRisk of amplifying existing societal biases (algorithmic bias).
Unclear accountability for AI-related adverse events.
Difficulty with practical workflow integration.
Risk of “futile technologization” (expensive tech with marginal benefit).		[19, 35, 37, 60]
Future directionsA guided research agendaThe SIR Foundation has prioritized HCC as a key use case.
Immediate research needs include tools for segmentation, simulation, and navigation.
A top priority is creating shared data commons to accelerate research.		[21]
Emerging technologiesShift toward powerful, adaptable foundation models.
Use of generative AI (e.g., ChatGPT) as a research tool.
Creation of “synthetic cohorts” to serve as control arms in clinical trials.		[19, 20, 64]
Ensuring quality & trustWidespread adoption of standardized reporting guidelines, such as the iCARE checklist, is essential to ensure future research is reproducible, transparent, and trustworthy.[19, 63]
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AI: artificial intelligence; IR: interventional radiology; HCC: hepatocellular carcinoma; iCARE: Interventional Radiology Reporting Standards and Checklist for Artificial Intelligence Research Evaluation.