Related Experiment Video
Updated: May 1, 2026
08:11
Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model
Published on: November 19, 2017
10.6K
Large Language Model and Pediatric Neurosurgery - A Neurosurgeon's Perspective on the Artificial Nervous System
Woon Tak Yuh1, Seung-Jun Ryu2,3, Tae-Shin Kim4,5
1Center for Spine Surgery, Davos Hospital, Yongin, Korea. woontak.yuh@gmail.com.
Journal of Korean Neurosurgical Society
|April 30, 2026
Summary
Large language models (LLMs) are transforming pediatric neurosurgery by mirroring brain development. Physicians will become "responsible interpreters," guiding AI while retaining ethical authority for human-centered care.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Pediatric Neurosurgery
Background:
- Large language models (LLMs) are rapidly advancing, impacting healthcare.
- Their specific implications for pediatric neurosurgery are not yet fully understood.
- A neuroscientific perspective can illuminate LLM development and potential.
Purpose of the Study:
- To interpret the evolution of LLMs through a neuroscientific lens relevant to pediatric neurosurgeons.
- To explore the current capabilities and limitations of LLMs for artificial general intelligence.
- To illustrate the transformative potential of LLMs in pediatric neurosurgery practice, research, and education.
Main Methods:
- A narrative review approach comparing LLM development to human brain maturation and cognitive processes.
- Analysis of LLM architectural and training parallels with neurodevelopmental stages.
- Examination of AI capabilities needed for artificial general intelligence mapped to neural functions.
- Illustration using a pediatric medulloblastoma case study.
Main Results:
- LLM architecture and training stages show parallels with prefrontal cortex maturation and synaptic development.
- Reasoning capabilities in LLMs emerge analogously to deliberate cognitive processing.
- Current LLMs require further development in continual learning, multimodal perception, and self-awareness for advanced applications.
- Frontier models offer multimodal reasoning but pediatric neurosurgery's "long-tail" challenges necessitate domain-specific augmentation.
Conclusions:
- LLMs and emerging AI systems are shifting from advisory tools to executing partners.
- Physicians will evolve into "responsible interpreters" of AI, maintaining judgment and ethical oversight.
- The unique human elements of metacognition and emotion are crucial in pediatric neurosurgery, enabling human-centered care.
- Pediatric neurosurgeons can actively shape AI integration, ensuring it enhances rather than replaces human expertise.
