Clinical Scorecard: The Translational Dilemma of Artificial Intelligence in Hepatocellular Carcinoma: From Complex Algorithm Development to Practical Clinical Application
At a Glance
Category
Detail
Condition
Key Mechanisms
Integration of AI for improved diagnostics and management, addressing intratumoral heterogeneity, domain shifts, and challenges in real-world applications.
Target Population
Care Setting
Key Highlights
AI enhances tumor delineation and predictive modeling in HCC.
Complex AI models face challenges in real-world clinical applications due to data dependency.
Traditional Cox models remain competitive in low-dimensional survival predictions.
Need for interpretable AI architectures to improve clinical utility.
Integration of AI in clinical trials is essential for validation.
Need for interpretable AI models to enhance clinical utility.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Regularly assess AI model performance across diverse datasets.
Monitor for semantic drift in AI applications using natural language processing.
Address semantic drift in AI applications.
Risks
Patient & Prescribing Data
Individuals diagnosed with Hepatocellular Carcinoma.
AI can refine screening protocols and enhance individualized treatment approaches.
Clinical Best Practices
Encourage multicenter collaborations to enhance data diversity.
Focus on developing interpretable AI models for clinical use.
Integrate AI technologies into Phase II randomized trials for validation.
