TBI-NeuroHELM: A Large Language Model Medical Benchmark for Traumatic Brain Injury Assessment

TBI-NeuroHELM is a large language model (LLM) benchmark framework designed for neurological assessment of traumatic brain injury (TBI). Drawing on the MedHELM methodology, it fills the gap in medical AI evaluation in the field of neurological diseases, providing researchers with a standardized testing tool to facilitate the reliability assessment of medical AI models in TBI scenarios.

Traumatic brain injury (TBI) is one of the leading causes of death and long-term disability worldwide. Traditional assessments rely on doctors' experience and scales, but professional evaluation is hard to access in resource-poor areas. In recent years, LLMs have shown great potential in the medical field, but there is a lack of professional evaluation benchmarks for neurological diseases. TBI-NeuroHELM emerged to build a complete benchmark system and fill this gap.

Benchmark Design

Adopting MedHELM-style multi-dimensional evaluation, covering:

1. Clinical knowledge understanding (pathophysiology, clinical manifestations, grading standards)
2. Diagnostic reasoning ability (accuracy of differential diagnosis of symptoms)
3. Treatment plan recommendations (acute phase management, surgical indications, rehabilitation programs)
4. Prognosis assessment (functional recovery, complication risk prediction)

Dataset Construction

Supports extracting structured information from medical literature and clinical guidelines to build a standardized dataset covering the full spectrum of TBI cases, including data processing and chart generation scripts.

Evaluation Metrics

Uses multi-dimensional metrics such as accuracy, F1 score, clinical relevance score, and safety assessment.

1. Medical AI R&D: Provides standardized evaluation tools for research teams to identify model knowledge gaps and reasoning flaws.
2. Clinical decision support systems: Helps hospitals screen AI models suitable for integration to ensure reliability in TBI scenarios.
3. Medical education and training: Evaluates the applicability of AI-assisted teaching tools in neurology education.

The innovation of TBI-NeuroHELM lies in its deep focus on the vertical domain, addressing the specificity of TBI assessment:

• Dynamic disease course characteristics: Considering the time dimension of rapid changes in patients' neurological status
• Multi-modal information integration: Needing to combine imaging, laboratory tests, and clinical manifestations
• Emergency decision-making scenarios: Supporting rapid and accurate judgment of acute TBI Different from general medical benchmarks, it is more aligned with clinical needs for TBI.

MedHELM is a medical LLM evaluation framework launched by institutions such as Stanford, emphasizing real clinical task assessment. TBI-NeuroHELM inherits this concept and refines the evaluation granularity to the subfield of neurological diseases, forming a more professional evaluation tool.

With the development of Chinese medical LLMs (such as MedGPT, Huatuo GPT), localized medical evaluation benchmarks are crucial. The methodology of TBI-NeuroHELM is worth learning:

1. Deep cultivation in vertical domains: Building professional evaluation tools for specific diseases
2. Clinical-oriented design: Evaluation tasks are close to real clinical scenarios
3. Open-source collaboration: Promoting continuous improvement of benchmarks through open-source communities.

TBI-NeuroHELM represents the trend of medical AI evaluation moving from general to professional, and is a key infrastructure to ensure the safety and effectiveness of AI in healthcare. In the future, it is expected to expand to fields such as stroke, epilepsy, and neurodegenerative diseases, building an AI evaluation system covering the entire field of neurology.