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Assessing and Optimizing Large Language Models on Spondyloarthritis Multi-Choice Question Answering: Protocol for

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This study develops a large language model (LLM) to improve the diagnosis and treatment of spondyloarthritis (SpA). The goal is to aid physicians, especially in under-resourced areas, for earlier and more accurate SpA detection.

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AIAI chatbotAI-assistant diagnosisartificial intelligencebenchmarklarge language modelspondyloarthritis

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Diagnostics
  • Computational Linguistics

Background:

  • Spondyloarthritis (SpA) is a complex inflammatory condition affecting the spine and sacroiliac joints, often leading to disability.
  • Diagnostic challenges in SpA are amplified in non-specialist settings, causing delays and misdiagnoses.
  • Large Language Models (LLMs) offer potential for improving SpA diagnosis, but SpA-specific models and benchmarks are currently lacking.

Purpose of the Study:

  • To develop a foundational medical LLM and a comprehensive evaluation benchmark for SpA diagnosis and treatment.
  • To enhance LLM capabilities through supervised fine-tuning for SpA-specific applications.
  • To support physicians in SpA diagnosis and treatment, particularly in resource-limited environments, and promote early detection in primary care.

Main Methods:

  • Creation of a 222-question multiple-choice benchmark for evaluating LLM performance in SpA diagnostics and therapeutics.
  • Selection and refinement of leading foundational models using public datasets, with the best performer undergoing further training.
  • Enhancement of LLM training with over 80,000 real-world patient cases using supervised fine-tuning and low-rank adaptation.

Main Results:

  • Model development is ongoing, with significant advancements expected by early 2024.
  • The SpA evaluation benchmark and initial performance results are scheduled for release in Q2 2024.

Conclusions:

  • The developed LLM aims to leverage complex clinical data analysis for precise SpA detection, diagnosis, and treatment.
  • This innovation is expected to reduce disabilities linked to delayed or incorrect SpA diagnoses.
  • Widespread adoption of the model across healthcare settings is anticipated to improve SpA management and patient outcomes.