From large language models to artificial general intelligence: Evolution pathways in clinical healthcare

This article examines the trajectory and challenges of evolving current large language models (LLMs) toward artificial general intelligence (AGI) capabilities within clinical healthcare environments. The text analyzes the gaps between contemporary LLMs' pattern recognition abilities and the robust reasoning, causal understanding, and contextual adaptation required for true medical AGI. Through a systematic review of current clinical applications and limitations of LLMs, the article identifies three critical areas requiring advancement: dynamic integration of multi-modal medical data streams, consistent medical reasoning across novel scenarios, and autonomous learning from clinical interactions while maintaining safety constraints. A novel architectural framework is proposed that combines LLM capabilities with symbolic reasoning, causal inference, and continual learning mechanisms specifically designed for clinical environments. The article suggests that while LLMs provide a promising foundation, achieving AGI in clinical systems requires fundamental breakthroughs in areas including knowledge representation, uncertainty quantification, and ethical decision-making. The article concludes by outlining a roadmap for research priorities and safety considerations essential for progressing toward clinical AGI while maintaining patient safety and care quality.