Revolutionizing viral hepatitis management: Artificial intelligence-assisted diagnosis and personalized treatment

Abstract

Viral hepatitis, including hepatitis B and hepatitis C (HCV), remains a significant global health burden, leading to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Traditional diagnostic methods, while effective, often face limitations in accuracy, accessibility, and timeliness. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, enhancing the detection, diagnosis, and treatment of viral hepatitis. This review explores the role of AI in viral hepatitis management, focusing on early detection through image analysis, digital pathology, and machine learning algorithms. AI-driven image analysis tools, such as convolutional neural networks, have demonstrated high accuracy in detecting HCV-related liver lesions from computed tomography scans. Supervised learning models such as support vector machines and hybrid quantum neural networks further enhance early risk stratification. AI also facilitates personalized treatment by predicting treatment responses, accelerating drug discovery, and advancing precision medicine. Furthermore, AI contributes to epidemiological surveillance by predicting disease spread and tracking treatment adherence. Despite its potential, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure equitable and effective AI implementation. Future directions include integrating AI into clinical workflows and expanding AI applications in low-resource settings. AI-assisted diagnosis and management have the potential to revolutionize viral hepatitis care, improving patient outcomes and reducing the global disease burden.

Key Words: Artificial intelligence; Viral hepatitis; Machine learning; Early diagnosis; Precision medicine

Core Tip: Artificial intelligence (AI) is transforming the diagnosis and management of viral hepatitis by enhancing early detection, optimizing treatment strategies, and supporting public health efforts. AI-driven radiology, histopathology, and machine learning models improve diagnostic accuracy, while AI-assisted drug discovery and precision medicine enable personalized treatment approaches. Despite its promise, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to facilitate widespread clinical adoption. The integration of AI into clinical workflows and its application in low-resource settings offer significant potential to reduce the global burden of viral hepatitis. Future integration of AI into national hepatitis screening programs and clinical guidelines may standardize precision care across diverse healthcare settings.

INTRODUCTION

Viral hepatitis, particularly hepatitis B (HBV) and hepatitis C (HCV), continues to pose a major global health challenge, leading to severe liver diseases such as fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). According to the World Health Organization, an estimated 296 million people were living with chronic HBV infection in 2019, and approximately 58 million people had chronic HCV infection, with millions remaining undiagnosed. Despite these staggering numbers, diagnostic and therapeutic gaps persist: As of 2022, only 13% of chronic HBV cases were diagnosed, and a mere 3% received antiviral therapy. For HCV, merely 36% of infections were diagnosed between 2015–2022, with only 20% accessing curative treatment. These disparities underscore critical bottlenecks in linking diagnosis to care delivery. Early and accurate diagnosis is crucial for effective management, yet conventional diagnostic methods, including serological testing and liver biopsies, have limitations in accessibility, accuracy, and invasiveness. Artificial intelligence (AI) has emerged as a powerful tool in medical research and clinical practice, offering innovative solutions to enhance the detection and treatment of viral hepatitis. This review explores AI applications in early diagnosis, personalized treatment, and public health management while addressing challenges and future directions (Table 1).

Table 1 Artificial intelligence techniques in viral hepatitis management.

AI technique	Application	Accuracy/Achievement
Deep learning	Image analysis for liver abnormalities	High accuracy in detecting HCV-related lesions (Vijayakumar[1], 2023)
SVM and KNN algorithms	Patient classification based on liver function tests	High accuracy in HCV diagnosis (Hiyari et al[5], 2024) (Venkatesan et al[6], 2023)
Hybrid quantum neural networks	Early detection of HCV from CT scans	Improved accuracy and speed (Vijayakumar[1], 2023)
C5.0 algorithm with chi-square	HCV classification	96.75% accuracy (Mahmud et al[7], 2024)
Fuzzy Logic	HBV diagnosis	94.35% accuracy (Singh et al[14], 2024)
Genetic neural network	HBV diagnosis	99.14% accuracy (Singh and Kaur[14], 2024)

HCV: Hepatitis C virus; HBV: Hepatitis B virus; CT: Computed tomography; SVM: Support vector machine; KNN: K-Nearest Neighbor; C5.0: A decision tree algorithm for classification.

AI IN EARLY DETECTION AND DIAGNOSIS OF VIRAL HEPATITIS

AI has significantly improved the early detection of viral hepatitis through advanced image analysis, digital pathology, and machine learning algorithms.

One of the most impactful applications is AI-driven image analysis in radiology. Deep learning models can analyze medical imaging modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging to detect liver fibrosis, cirrhosis, and HCC in patients with viral hepatitis. AI algorithms have demonstrated high accuracy in identifying HCV-related liver lesions from CT scans, surpassing traditional radiological assessments[1]. Emerging AI-driven optical imaging techniques, such as Mueller matrix polarimetry, have demonstrated high diagnostic accuracy in detecting HBV[2]. This approach complements traditional radiology and pathology by leveraging optical biomarkers and AI for early-stage viral detection. Additionally, automated fibrosis staging using AI-powered elastography has minimized the need for liver biopsies, providing a non-invasive and rapid diagnostic approach.

The integration of AI with digital pathology has enhanced histopathological evaluation of liver specimens. AI-based image analysis tools can detect HBV- and HCV-induced liver damage with an accuracy comparable to expert pathologists. Deep learning models trained on large datasets of histological slides can assess necroinflammation, fibrosis progression, and even early-stage carcinogenesis, aiding in diagnosis and prognosis evaluation[3].

Beyond imaging and pathology, machine learning algorithms play a crucial role in predicting disease progression and identifying high-risk patients. For HBV patients' antiviral treatment screening, a machine-learning integrated model combines clinical parameters (Alanine aminotransferase, HBV DNA, Fibroscan values) and immune indicators (NKbright cell surface molecule expression, CD3+ T cell activity). It remarkably enhances risk stratification accuracy (area under the curve = 0.997). In follow-up, the model found 10.8% of "non-treatment-eligible" patients needed intervention, decreasing the risk of liver disease progression from delayed treatment[4]. Supervised learning models such as Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) have demonstrated high accuracy in classifying patients based on liver function test results, enabling early intervention[5,6]. Supervised learning models (e.g., SVM, KNN) rely on labeled datasets for classification, whereas unsupervised approaches (e.g., clustering) identify hidden patterns in unannotated data. Moreover, advanced deep learning approaches, including hybrid quantum neural networks and C5.0 algorithms, have improved HCV classification accuracy, reducing diagnostic delays[7]. Hybrid quantum neural networks represent an emerging paradigm combining classical deep learning with quantum computing principles.

AI IN PERSONALIZED TREATMENT AND MANAGEMENT

Beyond diagnosis, AI has significantly contributed to personalized treatment approaches for viral hepatitis, improving treatment response prediction, drug discovery, and precision medicine strategies.

Machine learning models can predict individual patient responses to antiviral therapies, helping clinicians tailor treatment regimens. AI-driven predictive models have been developed to anticipate HCV treatment failure and the risk of hepatic encephalopathy—conditions lacking reliable prediction tools[8]. By analyzing patient-specific data, including virological and biochemical markers, AI models optimize therapeutic decisions, thereby reducing adverse effects and enhancing outcomes.

AI has also accelerated the drug discovery process for viral hepatitis. High-throughput screening combined with predictive modeling allows researchers to identify novel antiviral compounds with high efficacy, significantly reducing the time and cost of traditional drug development. AI-based in silico models have successfully predicted the effectiveness of direct-acting antivirals (DAAs) and identified potential resistance mutations in HCV[9]. Predictive models (e.g., logistic regression) stratify patients by HCC risk, while prescriptive AI tools (e.g., reinforcement learning) dynamically optimize DAA dosage based on real-time viral kinetics.

Furthermore, AI enhances precision medicine by integrating genomic, proteomic, and metabolomic data to develop personalized treatment plans. Next-generation sequencing and mass spectrometry have provided new insights into viral-host interactions, enabling targeted therapy development. AI-assisted vaccine design is another promising area, allowing for the creation of customized immunotherapies based on an individual's genetic predisposition and viral strain characteristics[10].

AI IN EPIDEMIOLOGICAL SURVEILLANCE AND PUBLIC HEALTH

AI is also playing an essential role in epidemiological surveillance and public health management by predicting disease spread, tracking treatment adherence, and guiding public health interventions.

AI-powered models can analyze epidemiological data to forecast hepatitis outbreaks and identify high-risk populations. These models utilize real-time health records, demographic trends, and environmental factors to enhance early warning systems and optimize resource allocation[9]. Such predictive capabilities are particularly valuable in guiding vaccination campaigns and improving HBV screening programs in endemic regions.

Moreover, AI-driven digital health platforms can monitor treatment adherence and detect medication resistance in patients undergoing antiviral therapy. By leveraging machine learning algorithms, AI can predict patient compliance patterns, allowing healthcare providers to intervene early and adjust treatment plans accordingly[9]. The implementation of AI-based mobile applications and wearable devices has further improved remote patient monitoring, ensuring sustained treatment success.

CHALLENGES AND LIMITATIONS

Despite the transformative potential of AI in viral hepatitis management, several challenges must be addressed to facilitate its widespread adoption.

Data privacy and security concerns remain a significant hurdle, as AI-driven healthcare applications require access to vast amounts of patient data. Ensuring compliance with data protection regulations and employing robust encryption techniques are essential to maintaining patient confidentiality[9,11].

Algorithmic bias poses another challenge, as AI models trained on non-representative datasets may yield inaccurate or inequitable predictions. Addressing this issue requires diverse and inclusive training datasets to improve model generalizability and fairness[9].

Regulatory compliance is crucial for integrating AI into clinical practice. AI models must undergo rigorous validation and regulatory approval to ensure their safety and effectiveness. Establishing standardized validation platforms will be critical in facilitating clinical adoption[12,13].

FUTURE DIRECTIONS

The future of AI in viral hepatitis management is promising, with ongoing research focused on overcoming current limitations and exploring new applications.

Integrating AI into clinical workflows will be essential for seamless collaboration between healthcare professionals and AI systems, improving diagnostic accuracy and reducing medical errors[9,12].

Additionally, expanding AI applications to low-resource settings has the potential to bridge healthcare disparities. AI-powered portable diagnostic tools and telemedicine platforms can improve access to hepatitis screening and treatment in underserved populations, enhancing global disease control efforts[1,14].

CONCLUSION

AI-assisted diagnosis and management are transforming the landscape of viral hepatitis care. By enhancing early detection, personalizing treatment plans, and supporting epidemiological surveillance, AI holds the potential to improve patient outcomes and reduce the global burden of HBV and HCV. However, addressing challenges such as data privacy, algorithmic bias, and regulatory compliance is critical to ensuring the safe and equitable implementation of AI in clinical practice. With ongoing advancements, AI is set to revolutionize viral hepatitis management, paving the way for a future of more effective and accessible healthcare solutions. To translate AI advancements into practice, policymakers should prioritize integrating validated AI tools into World Health Organization hepatitis care guidelines, such as incorporating AI-driven fibrosis staging into routine diagnostic protocols by 2030.