Evolving and novel applications of artificial intelligence in interventional oncology

Table 1 Artificial intelligence applications by procedural phase in interventional oncology

IO phase	AI application
Pre-procedural	Lesion segmentation (CNNs, radiomics)
	Needle path planning (CT-guided)
	Outcome prediction (e.g., TACE response, recurrence risk)
	Imaging enhancement (denoising, SNR/CNR improvement)
Intra-procedural	Real-time motion correction. Image fusion (CT/US)
	Needle tracking and trajectory optimization
	Multimodal image registration
	Robotic assistance
Post-procedural	Structured reporting (NLP, LLMs)
	Margin assessment (3D modeling, deformable registration)
	Recurrence prediction (radiomics, XGBoost)
	Longitudinal lesion tracking
System-level/patient-centered	Predictive maintenance (equipment)
	Workflow optimization (scheduling, triage)
	Patient education (AR tours, chatbots)
	Research support (trial matching, literature mining)

IO: Interventional oncology; AI: Artificial intelligence; CNN: Convolutional neural network; CT: Computed tomography; TACE: Transarterial catheter embolization; SNR: Signal-to-noise ratio; CNR: Contrast-to-noise ratio; US: Ultrasound; NLP: Natural language processing; LLMs: Large language models; 3D: Three-dimensional; XGBoost: Extreme gradient boosting; AR: Augmented reality.