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Training Language Models for Estimating Priority Levels in Ultrasound Examination Waitlists: Algorithm Development

Kanato Masayoshi1, Masahiro Hashimoto1, Naoki Toda1

  • 1Department of Radiology, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Tokyo, Japan, 81 3-3353-1211 ext 62477.

JMIR AI
|July 22, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence language models can accurately estimate medical examination request priorities, matching human radiologist performance. This demonstrates AI

Keywords:
clinical informaticshealth resourceshospital information systems.large language modelmachine learningnatural language processingultrasonography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Ultrasound examinations are essential but face availability limitations, necessitating hospital reservation and waitlist systems.
  • Current waitlist systems often lack automated prioritization, requiring manual review of free-form text requests.
  • This manual process is time-consuming and can be a bottleneck in patient care.

Purpose of the Study:

  • To investigate the feasibility of using AI language models for prioritizing medical examination requests.
  • To evaluate the performance of various language models in processing Japanese medical texts for priority estimation.
  • To identify challenges in applying AI to clinical workflow prioritization.

Main Methods:

  • Retrospective collection of 2,335 ultrasound examination requests from Keio University Hospital (Jan 2020-Mar 2023).
  • Fine-tuning of four language models (JMedRoBERTa, Luke, OpenCalm, LLaMA2) using two methods: final layer tuning and full layer tuning.
  • Evaluation of model performance using Kendall coefficient, compared against radiologist re-evaluation.

Main Results:

  • JMedRoBERTa achieved the highest performance when only the final layer was tuned (Kendall coefficient=0.225).
  • With full fine-tuning, JMedRoBERTa remained superior (Kendall coefficient=0.254), outperforming other models and radiologist re-evaluation (Kendall coefficient=0.221).
  • Results indicate successful adaptation of general-purpose models to specialized Japanese medical texts.

Conclusions:

  • AI language models can effectively estimate medical examination request priorities with accuracy comparable to human radiologists.
  • Fine-tuning enables general AI models to process domain-specific medical texts, showing promise for clinical applications.
  • Future work should address rank ambiguity, multi-institutional data, and explore advanced language models.