Artificial Intelligence: An Emerging Tool for Studying Drug-Induced Liver Injury.

Area of Science:

• Hepatology
• Medical Informatics
• Artificial Intelligence

Background:

• Drug-induced liver injury (DILI) presents diagnostic challenges due to its varied clinical manifestations and lack of specific biomarkers.
• DILI poses a significant public health risk, necessitating advanced research methodologies for better understanding and management.
• The complexity of DILI requires sophisticated analytical tools to interpret large datasets effectively.

Purpose of the Study:

• To review the current applications of artificial intelligence (AI) in drug-induced liver injury (DILI) research.
• To explain fundamental AI concepts and their relevance to DILI investigations.
• To explore the potential of AI, including natural language processing (NLP) and large language models (LLMs), in clinical DILI settings.

Main Methods:

• Systematic review of AI applications in DILI research.
• Explanation of core AI principles and subfields.
• Analysis of AI-driven approaches for risk stratification, prognosis, and causality assessment in DILI.
• Discussion of NLP and LLM integration in clinical practice for DILI.

Main Results:

• AI demonstrates significant potential in enhancing DILI research through complex model construction.
• AI-based methods show promise in risk stratification, prognostic evaluation, and causality assessment for DILI.
• NLP and LLMs are emerging tools for clinical applications in managing DILI.

Conclusions:

• AI represents a powerful and evolving tool for advancing the understanding and management of drug-induced liver injury.
• Further research and adoption of AI methodologies are crucial for addressing the challenges in DILI diagnosis and treatment.
• The integration of AI, particularly NLP and LLMs, holds promise for improving clinical decision-making in DILI cases.