Evolving and novel applications of artificial intelligence in interventional oncology

Table 3 Human-centered artificial intelligence features for enhancing clinician trust and ensuring safe deployment

Feature	Purpose	Example	Deployment considerations
Feature attribution	Identifies the imaging or clinical features that most influenced the AI’s recommendation	In ablation planning, feature attribution can highlight lesion boundaries, proximity to critical structures, or perfusion metrics that guided probe placement	Should be integrated into procedural consoles with toggleable overlays for real-time validation
Uncertainty quantification	Provides confidence scores or probability distributions to help clinicians assess risk and determine whether to rely on or override the output	During catheter navigation, an AI system might suggest a path with 92% confidence, giving the proceduralist a quantifiable basis for trust	Must be displayed in plain language (e.g., “low confidence”) and updated dynamically during the procedure
Saliency maps or visual overlays	Highlight relevant anatomical regions on live imaging by overlying AI-derived insights (e.g., tumor margins, vessel segmentation) to support real-time targeting	Enhances targeting precision in ultrasound- or CT-guided procedures by showing which regions the AI model considers most relevant	Requires seamless integration with imaging feeds and adjustable settings
Counterfactual examples	Illustrate how small changes in input (e.g., lesion size or location) would alter the AI’s recommendation, helping assess model robustness	Could be used pre-procedurally to simulate alternative probe placements or embolization strategies	Should be available pre-procedurally for simulation and intra-procedurally for real-time adjustment

AI: Artificial intelligence; CT: Computed tomography.