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Related Experiment Video

Updated: May 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Personalized Case- and Evidence-Based TBI Prognosis with Small Language Models.

Pranav Manjunath1, Syed M Adil2, Benjamin D Wissel2

  • 1Dept. of Biomedical Engineering, Duke University, Durham, USA.

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Traumatic Brain Injury l: Introduction01:28

Traumatic Brain Injury l: Introduction

DefinitionTraumatic brain injury, or TBI, is a disturbance of normal brain function induced by an external mechanical force, such as a direct blow to the head or a penetrating injury. It can affect both brain structure and function, producing a wide range of clinical outcomes. TBI is a heterogeneous condition, meaning its effects may differ based on the type, location, and severity of the injury.Basis of ClassificationTBI is classified based on severity, injury mechanism, or pathophysiology. In...

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This study introduces a dual retrieval AI framework for traumatic brain injury (TBI) patient disposition, combining small language models with clinical guidelines and similar patient cases for improved accuracy and personalized decision-making.

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Traumatic Brain Injury Management

Background:

  • Emergency department disposition for traumatic brain injury (TBI) relies on complex data synthesis, often using heuristics leading to variable outcomes.
  • Large language models (LLMs) offer potential for evidence-based practice but face limitations in size, cost, and privacy for clinical use.

Purpose of the Study:

  • To develop a dual retrieval-augmented framework using efficient, on-premise small language models (SLMs) for personalized TBI patient disposition.
  • To integrate evidence-based practice (guideline retrieval) with case-based reasoning (similar patient exemplars) for enhanced prediction.

Main Methods:

  • Implemented a dual retrieval framework with two open-source SLMs (Phi-4-mini, Qwen-2.5) under 4B parameters.
Keywords:
Case-based ReasoningED DispositionEvidence-based PracticeSmall Language ModelsTraumatic Brain Injury

Related Experiment Videos

Last Updated: May 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • Modeled evidence-based practice by retrieving patient-specific guideline passages.
  • Utilized case-based reasoning to retrieve similar patients as few-shot exemplars for personalized context.

    • Main Results:

    • Similar patient exemplars consistently improved classification performance (sensitivity without sacrificing specificity) across both SLMs.
    • Clinical guidelines had a smaller impact individually but shifted predictions towards guideline-consistent behavior when combined with exemplars.
    • Clinician evaluations indicated that exemplars enhance accuracy, while guidelines improve clinical relevance and justification of AI outputs.

    Conclusions:

    • Targeted retrieval personalizes AI predictions and their rationale, improving performance, interpretability, and trustworthiness in clinical decision-making.
    • The dual retrieval framework offers a viable approach for deploying AI in TBI disposition, balancing accuracy with clinical relevance.