Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications

Abstract

Alcohol-related liver disease (ARLD) remains a major public health concern, often diagnosed at advanced stages with limited treatment options. Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes. Artificial intelligence (AI) has emerged as a powerful tool for predicting ARLD, leveraging multi-omics data, machine learning algorithms, and non-invasive biomarkers. This review explores the current advancements in AI-driven ARLD prediction, highlighting key methodologies such as multi-omics data integration, gut microbiome-based modeling, and predictive analytics using machine learning techniques. AI models incorporating transcriptomics, proteomics, and clinical data have demonstrated high diagnostic accuracy, with some achieving an area under the curve exceeding 0.90. Furthermore, non-invasive biomarkers, including liver stiffness measurements and circulating proteomic panels, have been successfully integrated into AI frameworks for early detection and risk stratification. Despite these advancements, challenges such as data heterogeneity, model generalizability, and ethical considerations remain. Future directions include the development of advanced biomarker discovery, wearable and point-of-care AI-integrated technologies, and precision medicine approaches tailored to individual risk profiles. AI-driven models hold significant potential in transforming ARLD prediction and management, ultimately contributing to early diagnosis and improved clinical outcomes.

Key Words: Alcohol-related liver disease; Artificial intelligence; Machine learning; Multi-omics data; Non-invasive biomarkers

Core Tip: Artificial intelligence (AI) has emerged as a transformative tool for early prediction of alcohol-related liver disease (ARLD). By integrating multi-omics data, gut microbiome analysis, and machine learning algorithms, AI models have achieved high diagnostic accuracy and predictive capability. This review explores key studies, methodologies, and clinical applications of AI in ARLD prediction, addressing challenges such as data heterogeneity and model generalizability. The future of AI in ARLD lies in advanced biomarker discovery, wearable technology, and personalized medicine approaches.

INTRODUCTION

Alcohol-related liver disease (ARLD) is a significant global health concern, accounting for substantial morbidity and mortality. The disease progresses through various stages, including steatosis, alcoholic hepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma, with late-stage diagnosis often resulting in poor prognoses[1]. Early detection and intervention are crucial for improving patient outcomes, yet conventional diagnostic methods rely on invasive liver biopsies or indirect biomarkers with limited sensitivity.

Artificial intelligence (AI) has demonstrated remarkable potential in enhancing ARLD prediction and management. Machine learning models leveraging multi-omics data, imaging, and clinical parameters can identify high-risk individuals before the onset of advanced disease. AI-based risk stratification has been shown to outperform traditional scoring systems such as the model for end-stage liver disease (MELD) score[2]. This review explores the role of AI in ARLD prediction, focusing on multi-omics data integration, machine learning methodologies, non-invasive biomarkers, and clinical applications, while addressing existing challenges and future directions (Table 1).

Table 1 Key studies on artificial intelligence in alcohol-related liver disease prediction.

Study focus	Methodology	Key findings	Citation
Multi-omics integration	Machine learning with transcriptomics and proteomics data	90% accuracy for liver tissue classification, 89% for PBMCs	(Listopad et al[4], 2024) (Listopad et al[3], 2022)
Gut microbiota-based diagnosis	Supervised ML algorithms with feature reduction techniques	AUCs > 0.90 for ALD and NAFLD diagnosis	(Park et al[5], 2024)
Gradient boosting for mortality	Gradient boosting with laboratory and microbiome data	AUC of 0.87 for 30-day mortality prediction, outperforming MELD score	(Gao et al[2], 2022)
Proteomic panels for fibrosis	Machine learning on liver and plasma proteomics data	AUC of 0.90 for detecting F2 or greater fibrosis	(Mezzacappa and Bhat[8], 2023)
Bayesian optimization for classifiers	Bayesian optimization of Random Forest and XGBoost	81.06% accuracy for liver disease prediction	(Kumar and Rani[7], 2024)
Extra Tree for early detection	Extra Tree algorithm with oversampling techniques	92% accuracy for early liver disease detection	(Lima et al[10], 2024)

ALD: Alcoholic-associated liver disease; AUC: Area under the curve; ML: Machine learning; NAFLD: Non-alcoholic fatty liver disease; PBMCs: Peripheral blood mononuclear cells. MELD: Model for end-stage liver disease; XGBoost: eXtreme Gradient Boosting.

ROLE OF AI IN PREDICTING ARLD

Multi-omics data integration

AI models integrate diverse multi-omics datasets, including transcriptomics, proteomics, and gut microbiota, to enhance ARLD prediction accuracy (Table 2). Recent studies have demonstrated that combining transcriptomic and proteomic data from liver tissue and peripheral blood mononuclear cells (PBMCs) enables precise classification of alcohol-associated hepatitis and cirrhosis, achieving 90% accuracy for liver tissue and 89% for PBMCs[3,4].

Table 2 Comparative analysis of artificial intelligence models in alcohol-related liver disease prediction.

AI model	Methodology	Performance metrics	Key limitations	Citation
Gradient Boosting	MICE imputation, SMOTE, feature selection	AUC = 0.87 (30-day mortality prediction)	Small sample size, Lack of external validation, missing data	Gao et al[2], 2022
Stacked Ensemble (XGBoost + Logistic Regression)	Multi-omics + clinical features	Accuracy = 93.86% (alcoholic cirrhosis prediction)	Lack of external validation	Vinutha et al[6], 2022
Random Forest/XGBoost	Bayesian optimization	Accuracy = 81.06% (Random Forest), 79.85% (XGBoost)	Data heterogeneity	Kumar and Rani[7], 2024
Extra Tree with Oversampling	Liver stiffness + clinical data	Accuracy = 92% (early ARLD detection)	Requires high-resolution imaging	Lima et al[10], 2024

AUC: Area under the curve; MICE: Multivariate imputation by chained equations; SMOTE: Synthetic minority oversampling technique; XGBoost: eXtreme Gradient Boosting; ARLD: Alcohol-related liver disease; AI: Artificial intelligence.

Gut microbiome composition is another promising biomarker for ARLD prediction. Machine learning models trained on gut microbiota profiles have achieved an area under the curve (AUC) exceeding 0.90 for differentiating ARLD from non-alcoholic fatty liver disease[5]. These findings suggest that AI-driven multi-omics integration can improve early disease detection and stratification.

Machine learning algorithms in ARLD prediction

AI-based predictive modeling in ARLD utilizes various machine learning algorithms, including gradient boosting, ensemble learning, and Bayesian optimization, to enhance diagnostic accuracy and mortality risk assessment (Table 2).

Gradient boosting has been effectively applied to predict 30-day mortality in ARLD patients. A study by Gao et al[2] demonstrated that a gradient boosting model using laboratory and microbiome data achieved an AUC of 0.87, surpassing the predictive performance of the MELD score. Similarly, Vinutha et al[6] employed stacked ensemble models combining XGBoost and logistic regression, achieving 93.86% accuracy in predicting alcoholic liver cirrhosis.

Bayesian optimization techniques have also been explored to improve classifier performance. Kumar and Rani[7] optimized Random Forest and XGBoost models, achieving predictive accuracies of 81.06% and 79.85%, respectively, demonstrating the efficacy of AI-driven approaches in ARLD diagnosis and prognosis.

Non-invasive biomarkers and AI-based prediction

AI models integrating non-invasive biomarkers provide promising alternatives to liver biopsies. Proteomic biomarker panels and imaging-based assessments have been effectively incorporated into AI frameworks for early ARLD detection.

Proteomic studies have identified circulating biomarkers with superior diagnostic performance compared to conventional tests for fibrosis, inflammation, and steatosis. Mezzacappa and Bhat[8] reported that liver-plasma proteomics outperformed existing clinical assays in detecting fibrosis progression. Liver stiffness measurement via transient elastography has also been validated as an AI-assisted tool for predicting ARLD decompensation and mortality[9].

Additionally, AI-driven imaging analysis has been leveraged to enhance diagnostic accuracy. Lima et al[10] demonstrated that an Extra Tree model utilizing oversampling techniques achieved 92% accuracy in early ARLD detection, reinforcing the value of AI in non-invasive disease monitoring.

CLINICAL APPLICATIONS OF AI IN ARLD PREDICTION

Early detection and risk stratification

AI-driven models have improved early identification and risk stratification of individuals prone to ARLD. Genetic predisposition plays a key role in disease susceptibility, and AI-powered polygenic risk scores (PRS) have demonstrated enhanced predictive accuracy. Schwantes-An et al[1] developed a PRS model for alcohol-associated cirrhosis, which outperformed traditional clinical predictors. Furthermore, proactive AI-based screening in high-risk populations, particularly those with alcohol use disorder, has been proposed as a strategy to detect advanced fibrosis and cirrhosis at earlier stages[11].

Personalized medicine and treatment optimization

Machine learning enables personalized risk assessment and treatment planning. AI-driven models can integrate patient-specific genetic and clinical data to tailor interventions, optimizing pharmacological therapies and monitoring responses to treatment. Kumar and Rani[7] demonstrated that Bayesian optimization improved ARLD predictive accuracy, allowing for individualized disease management. AI-guided decision-making is expected to revolutionize liver transplantation eligibility assessment and post-transplant monitoring.

Cost-effective screening for At-risk populations

AI-powered tools offer cost-effective alternatives to traditional liver disease screening. Non-invasive methodologies reduce the need for expensive and invasive procedures, improving accessibility in underserved populations. Lima et al[10] highlighted the utility of AI-driven Extra Tree models in early liver disease detection, providing an efficient and scalable solution for ARLD screening programs.

CHALLENGES AND LIMITATIONS

Despite the advancements in AI for ARLD prediction, several challenges remain.

Data heterogeneity poses a significant obstacle in AI model development. Integrating multi-omics, imaging, and clinical data from diverse sources requires standardized preprocessing and normalization techniques. Recent advancements in transfer learning (TL) offer promising solutions to mitigate these challenges. For instance, Long et al[12] developed a transfer learning radiomics model that combined pretrained ResNet50 features with radiomic signatures to predict tertiary lymphoid structures in hepatocellular carcinoma. Their model achieved robust performance (AUC = 0.85) across four multicenter validation cohorts, demonstrating TL’s ability to harmonize heterogeneous MRI data through techniques such as N4 bias correction and intensity normalization. This approach highlights how pretrained models can adapt to variable datasets, particularly in scenarios with limited or imbalanced samples (e.g., early-stage fibrosis in ARLD). Federated Learning (FL) enables collaborative model training across multiple institutions without sharing raw data, addressing privacy concerns and data variability. Recent studies in hepatic steatosis detection further exemplify the efficacy of FL in real-world heterogeneous data environments. For instance, Qi et al[13] implemented FL across simulated clinical sites using B-mode ultrasound images, evaluating four FL algorithms under diverse data partitioning scenarios. Their results demonstrated that FedAvg achieved an AUC of 0.93 (95%CI 0.92–0.94) in source-based partitioning, closely matching centralized model performance (AUC = 0.90). This highlights FL’s capacity to handle non-IID data while preserving privacy. Additionally, Dirichlet distribution-based allocation effectively addressed class imbalance, with global data balancing (e.g., equal class distribution) yielding stable AUCs (0.90) despite local heterogeneity. These findings underscore FL’s adaptability to data quantity skew and distribution mismatch, particularly when combined with normalization techniques. Such frameworks could be extended to ARLD prediction, enabling multi-center collaborations without compromising patient privacy. Future frameworks integrating TL and FL may offer comprehensive solutions to data heterogeneity and ethical concerns. The generalizability of AI models is another concern, as many studies are limited to small, single-center datasets. Multi-center validation studies with diverse populations are necessary to ensure broader applicability.

Ethical and regulatory considerations must also be addressed before AI implementation in clinical practice. Issues such as data privacy, model bias, and regulatory approval for AI-based diagnostics are critical barriers to widespread adoption. Ensuring transparency and fairness in AI-driven healthcare applications remains a priority.

FUTURE DIRECTIONS

The future of AI in ARLD prediction lies in further advancements in biomarker discovery, wearable technologies, and precision medicine approaches.

Integrating multi-omics data with AI can lead to the identification of novel biomarkers for early ARLD detection. Real-time monitoring using AI-powered wearable biosensors and point-of-care devices has the potential to transform disease management. Additionally, AI-driven risk stratification will enable personalized therapeutic strategies based on individual genetic and clinical profiles.

Continued research efforts should focus on refining AI algorithms, expanding multi-center collaborations, and addressing regulatory challenges to facilitate clinical translation. AI-driven approaches hold the promise of revolutionizing ARLD prediction and improving patient outcomes.

CONCLUSION

AI has significantly advanced the prediction and management of ARLD through multi-omics data integration, machine learning algorithms, and non-invasive biomarkers. AI-driven models have demonstrated superior diagnostic accuracy, outperforming traditional scoring systems in risk stratification and prognosis. While challenges such as data heterogeneity, model generalizability, and ethical concerns remain, AI offers transformative solutions for early detection, personalized medicine, and cost-effective screening. Future developments in AI and digital health technologies are expected to enhance ARLD prevention and treatment, ultimately improving patient care in hepatology.