The Inefficacy of Artificial Intelligence Large Language Models in Healthcare: A Clinical and Statistical Perspective

Objective: This perspective piece examines the role of Large Language Models (LLMs) in healthcare, arguing that despite significant investment, these models have had only a limited impact. Moreover, we argue that LLMs must replicate key phases of clinical healthcare delivery to be a force multiplier, a necessary condition to address the global burden of disease. Discussion: We argue that LLMs lack the metacognitive capacity for ranked, dynamic reasoning. This is evidenced by clinically dangerous fabrications and an inability to perform unless complete information is provided. We extend clinical critiques with a statistical argument and a simulation exercise demonstrating that LLM-based diagnosis is not merely impractical but structurally incapable of converging on correct diagnoses in realistic clinical settings. Conclusion: Unless LLMs can independently collect patient history and triage, eliminate differential diagnoses, provide a treatment plan, and generate encounter notes, these models will have limited gains in efficiency relative to cognitive AI and structured reasoning approaches that are capable of functioning autonomously at each stage of the clinical workflow.