Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: A short narrative review of diagnostic and therapeutic implications

Table 1 Artificial intelligence-assisted diagnosis in metabolic dysfunction-associated steatotic liver disease and related liver-disease settings

Ref.	Data/model	Reference standard and validation	Main result	Focused limitation
Corey et al[15]	Derivation cohort of 620 patients; adaptive LASSO logistic-regression algorithm using EHR narrative mentions, ICD-9 codes, and triglyceride levels; subsequently applied to 8458 individuals	Manual chart review with biopsy or clinical diagnosis as the reference standard	The combined algorithm achieved an AUC of 0.85, compared with 0.75 for ICD-9 codes alone, and identified an independent cohort of 8458 individuals with a high probability of historically defined NAFLD	Retrospective design; historical terminology; effects on clinical outcomes were not evaluated
Stuart et al[16]	NLP-supported EHR identification of 957 patients with MASLD over six months	Manual review of monthly cohorts; NLP identification of steatosis from abdominal imaging reports and structured assessment of MASLD criteria	Overall PPV exceeded 93%; the imaging-report NLP component achieved a PPV of up to 99.4%; only 140 of 957 patients (14.6%) had a MASLD-related diagnosis code	Single-health-system study; dependent on documentation and coding practices; no external validation
Lee et al[19]	Deep convolutional neural network trained on 13608 ultrasonography images from 3446 patients; internal test set of 266 patients and external test set of 572 patients	Pathological specimens or METAVIR scores estimated by transient elastography; comparison with five radiologists	Four-class METAVIR accuracy was 83.5% internally and 76.4% externally. Cirrhosis AUC was 0.901 internally and 0.857 externally; external performance exceeded that of the five radiologists	Not MASLD-specific; reference standards were heterogeneous; tertiary-centre transportability requires evaluation
Wang et al[23]	Prospective multicentre study of 398 patients and 1990 shear-wave elastography images from 12 hospitals; deep-learning radiomics model	Histological fibrosis staging in patients with chronic hepatitis B	AUCs were 097 for F4, 0.98 for ≥ F3, and 0.85 for ≥ F2; overall performance exceeded conventional two-dimensional shear-wave elastography and biomarkers for most fibrosis thresholds	Indirect evidence for MASLD because the study population had chronic hepatitis B
Forlano et al[24]	Machine-learning analysis of 246 liver biopsies: 100 in the derivation cohort and 146 in the validation cohort	Independent pathologist assessment using NASH Clinical Research Network criteria and manual histological annotations	In the derivation cohort, ICCs were 097 for steatosis, 0.96 for inflammation, 0.94 for ballooning, and 0.92 for fibrosis. Quantitative findings were subsequently confirmed in the validation cohort	Single-centre biopsy cohort; sampling variability and transportability across staining and scanner platforms remain concerns
You et al[18]	Retrospective cohort of 152 patients; 17 routine clinical and laboratory variables; comparison of ten machine-learning models using five-fold cross-validation	MRI-PDFF-defined steatosis as the quantitative reference standard	Logistic regression achieved the highest AUC of 0.873. Random forest achieved the highest accuracy of 0.816 and F1 score of 0.807	Small, single-centre Taiwanese cohort; internal validation only; prospective external validation and assessment of clinical decision thresholds are required

AUC: Area under the curve; EHR: Electronic health record; ICC: Intraclass correlation coefficient; ICD-9: International Classification of Diseases, Ninth Revision; LASSO: Least absolute shrinkage and selection operator; MASLD: Metabolic dysfunction-associated steatotic liver disease; MRI-PDFF: Magnetic resonance imaging proton-density fat fraction; NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis; NLP: Natural language processing; PPV: Positive predictive value.