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This review article examines the current and potential applications of artificial intelligence (AI), including machine learning and deep learning, in the diagnosis, prognostication, and management of hepatocellular carcinoma (HCC). AI models, particularly convolutional neural networks, are being used to improve diagnostic accuracy across imaging modalities (ultrasound, CT, MRI) and predict factors like microvascular invasion, recurrence, and treatment response to interventions like TACE and SBRT. The review highlights the potential for AI to personalize treatment and improve patient outcomes, while also acknowledging challenges to widespread adoption.
AI-driven tools show promise in enhancing the diagnosis, risk stratification, and personalized treatment planning for HCC, potentially improving outcomes by predicting factors like microvascular invasion and treatment response.
The integration of artificial intelligence (AI) into medicine, oncology, and radiology represents a marked shift in the diagnosis, prognostication, and management of hepatocellular carcinoma (HCC), a malignancy with high global incidence and poor prognosis. This review examines the application of AI, including machine learning (ML) and deep learning (DL), across the spectrum of HCC care. As AI advances, new convolutional neural networks (CNNs) and other models are enhancing diagnostic accuracy, reducing interpretation times, and improving the characterization of liver lesions across major imaging modalities including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). Beyond diagnosis, the transformative role of AI in prognostication is also improving, where AI can now noninvasively predict critical factors such as microvascular invasion, genetic mutation status, tumor recurrence, and treatment response. Furthermore, AI has shown promise in facilitating patient-specific treatment planning by stratifying patients for interventions such as transarterial chemoembolization (TACE) and stereotactic body radiation therapy (SBRT). The review also addresses the emerging fields of pathomics and the use of AI in positron emission tomography (PET), while critically evaluating the cost-effectiveness of these technologies. Despite its promise, the widespread clinical adoption of AI faces challenges, including limited generalizability, maintaining patient privacy, ethical considerations, and the need for robust prospective validation. Ultimately, this review illustrates that the future of HCC management lies in a collaborative, hybrid-intelligence model, where AI-driven insights augment clinical expertise to optimize diagnostic pathways, personalize therapy, and improve patient outcomes.
