Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation

Table 1 Summary of quality assessment based on Scale for the Assessment of Narrative Review Articles criteria

SANRA criterion	Summary of findings
Justification of article importance	High: Most studies clearly stated the clinical problem of HCC detection and the potential of DL
Statement of concrete aims	Medium/high: Modeling aims (e.g., classification accuracy) were usually clear; efficiency aims were sometimes less explicitly stated
Description of literature search	Low: A significant weakness across almost all primary studies; search strategies were rarely reported
Scientific accuracy and rigor	Medium: Methods were mostly sound technically, but clinical validation rigor (prospective/multicenter) was often low
Discussion of limitations	Medium: Common limitations like small sample size were often acknowledged; discussion of bias or generalizability was less frequent
Quality of illustrations/reporting	High: Most studies included high-quality figures of models, results, and attention maps. Efficiency metrics (e.g., model size, inference time) were not consistently reported

SANRA: Scale for the Assessment of Narrative Review Articles; HCC: Hepatocellular carcinoma; DL: Deep learning.