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Related Experiment Video

Updated: Jan 13, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model.

Nicholas Dietrich1, David McShannon2, Mark F Rzepka3

  • 1Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada.

PLOS Digital Health
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise for lung sound classification. Few-shot prompting with GPT-4o improved accuracy over zero-shot, though clinical use requires further development.

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Area of Science:

  • Artificial Intelligence
  • Biomedical Signal Processing
  • Respiratory Medicine

Background:

  • Traditional deep learning for lung sound analysis needs extensive labeled data.
  • Multimodal large language models (LLMs) offer a potential prompt-based alternative.
  • Evaluating general-purpose LLMs for respiratory sound classification is crucial.

Purpose of the Study:

  • To assess GPT-4o's utility for lung sound classification using mel-spectrograms.
  • To compare few-shot prompting against zero-shot prompting for this task.
  • To establish a baseline for prompt-based multimodal inference in respiratory acoustics.

Main Methods:

  • Converted 6898 annotated respiratory cycles from ICBHI 2017 database into mel-spectrograms.
  • Employed GPT-4o with both zero-shot and few-shot prompting strategies for classification.
  • Evaluated performance using accuracy, precision, recall, F1-score, and McNemar's test.

Main Results:

  • Few-shot prompting yielded statistically significant improvements in accuracy (0.363 vs. 0.320) and other metrics.
  • Model repeatability analysis showed high agreement (κ = 0.76-0.88), indicating excellent consistency.
  • Performance gains were limited but statistically significant (p < 0.001).

Conclusions:

  • GPT-4o shows potential for prompt-based lung sound classification, with few-shot prompting outperforming zero-shot.
  • Current performance is insufficient for clinical deployment but provides a foundation for future research.
  • Prompt-based multimodal inference offers a flexible approach for spectrogram analysis in respiratory medicine.