Large Language Model and Pediatric Neurosurgery - A Neurosurgeon’s Perspective on the Artificial Nervous System

Abstract

Large language models (LLMs) are rapidly transforming healthcare, yet their implications for pediatric neurosurgery remain underexplored. This narrative review interprets LLM evolution through a neuroscientific lens familiar to pediatric neurosurgeons. We trace the parallel development of LLMs and the human brain: architecture evolving from sequential processing to attention mechanisms mirrors prefrontal cortex maturation; training stages parallel synaptic exuberance, adolescent pruning, and socialization; reasoning capabilities emerge through chain-of-thought prompting and reinforcement learning, analogous to deliberate cognitive processing. We then examine capabilities current LLMs lack but would require for artificial general intelligence-continual learning, multimodal perception, self-awareness, world models, and physical embodiment-mapping each to corresponding neural functions. Using a representative case of pediatric medulloblastoma, we illustrate how these technologies will reshape clinical practice, surgery, research, and education. Current frontier models deliver excellent medical performance with multimodal reasoning at low cost, yet pediatric neurosurgery presents a "long-tail" challenge where rare conditions and reliance on expert consensus demand domain-specific augmentation. The emergence of agentic artificial intelligence (AI), physical AI, and autonomous research systems signals a shift from AI as advisory tool to executing partner. LLMs externalize intellectual labor at unprecedented scale, redefining physicians as "responsible interpreters" who orchestrate AI while retaining judgment, accountability, and ethical authority. In pediatric neurosurgery, this role uniquely integrates metacognitive and emotional dimensions beyond AI's reach within the child-caregiver-physician relationship, enabling human-centered care and empowering neurosurgeons to actively shape AI integration rather than merely adapt to it.

Ähnliche Arbeiten