OBJECTIVES: This AI-generated poster aims to explore the potential of leveraging artificial intelligence (AI) techniques in health economics to enhance efficiency, accuracy, and decision-making. The objective was to identify key areas where AI could improve the quality control of economic models, streamline data analysis, and enhance evidence synthesis.

METHODS: Extensive literature review and analysis using AI algorithms were conducted to explore AI's potential in health economics. Various AI techniques for tasks such as model input validation, optimising code writing efficiency, literature summarisation, and graph digitisation were examined.

RESULTS: AI techniques demonstrate significant efficiencies in health economics tasks. AI can synthesise existing literature quicker and more efficiently than the average human. It automates model input validation, enhances quality control processes, and detects errors or inconsistencies. AI swiftly extracts relevant tables and figures from documents like Clinical Study Reports, expediting information retrieval. Additionally, AI excels in digitising graphs from images or scanned documents, enhancing accuracy and data extraction efficiency.

CONCLUSIONS: Integrating AI techniques holds tremendous promise for enhancing efficiency, accuracy, and decision-making in health economics. AI's ability to synthesise literature quickly and efficiently, automate model validation, expedite information retrieval, and improve data extraction accuracy signifies its potential in the field. Further research is necessary to fully implement and explore these AI applications in health economics. By harnessing the power of AI, health economics research can advance in quality control, data analysis, evidence synthesis, and decision-making.