Language Model

• Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors
• Reshaping neurosurgical training: a novel simulation-based concept for structured skill acquisition and curriculum integration
• The sulcal patterns of the occipito-temporal and anterior cingulate cortices influence reading and writing in children and adults
• Role for Left Dorsomedial Prefrontal Cortex in Self-Generated, but not Externally Cued, Language Production
• Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models
• Dynamic Causal Tractography Analysis of Auditory Descriptive Naming: An Intracranial Study of 106 Patients
• Do LLMs Have 'the Eye' for MRI? Evaluating GPT-4o, Grok, and Gemini on Brain MRI Performance: First Evaluation of Grok in Medical Imaging and a Comparative Analysis
• Knowledge Graph-Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation

A language model is a computational system designed to understand, generate, and manipulate text in a coherent and contextually appropriate manner. It is trained on large datasets of text to identify patterns, structures, and meanings in human language. Advanced examples include ChatGPT, GPT-4, and similar models based on deep learning techniques.

• Text Generation:
  1. Summary of documents.
  2. Content creation.
  3. Answering questions.
• Machine Translation:
  1. Translating text between multiple languages.
• Clinical and Scientific Processing:
  1. Analyzing medical data.
  2. Drafting reports or scientific articles.
• Virtual Assistants:
  1. Automating customer support.
  2. Assisting with administrative tasks.
• Education and Training:
  1. Personalized tutoring.
  2. Generating examples and explanations.
• Coding and Debugging:
  1. Assisting with programming tasks, including code generation and debugging.
• Research and Analysis:
  1. Identifying trends in large text datasets.

• Research Assistance:
  1. Quick access to relevant medical literature.
  2. Summarizing complex scientific articles.
• Medical Education:
  1. Generating customized educational materials for students or patients.
• Clinical Decision Support:
  1. Proposing protocols based on medical literature.
  2. Identifying potential diagnoses from clinical text.
• Administrative Task Automation:
  1. Drafting surgical or follow-up reports.
• Enhanced Communication:
  1. Translating and drafting documents in multiple languages.

Large Language Model.