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Related Experiment Video

Updated: May 23, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster

Julia Nancy Lo Cascio1, Nicholas Mourikis1, Chinyere J Okpara2

  • 1NYU Grossman Long Island School of Medicine (GLISOM).

Journal of Occupational and Environmental Medicine
|May 22, 2026
PubMed
Summary

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Large language models (LLMs) identified unregistered World Trade Center (WTC) lung cancer survivors. WTC-exposed residents faced higher risks of disease progression and death, with specific genetic links found.

Area of Science:

  • Environmental Health
  • Oncology
  • Computational Biology

Background:

  • World Trade Center (WTC) exposure is linked to adverse health outcomes, including lung cancer.
  • Existing registries may not fully capture all affected individuals.
  • Understanding the impact of WTC exposure on lung cancer progression is crucial.

Purpose of the Study:

  • To utilize large language models (LLMs) to identify an unregistered WTC-exposed lung cancer subpopulation.
  • To assess the impact of WTC exposure on the disease course of lung cancer patients.
  • To explore associations between WTC exposure, patient demographics, and clinical outcomes.

Main Methods:

  • Statistical analysis of associations between WTC exposure type and smoking history, cancer stage, mutation status, disease progression, and survival.
Keywords:
9/11World Trade Centerlarge language modelsprimary lung cancer

Related Experiment Videos

Last Updated: May 23, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

  • Utilizing LLMs for population identification and data extraction.

    • Main Results:

    • A higher proportion of never-smokers was found among residents compared to first responders and commuters (p = 0.005).
    • WTC-exposed residents demonstrated over double the risk of disease progression (HR = 2.14, p = 0.008) and an increased risk of death (HR = 2.43, p = 0.03).
    • Epidermal Growth Factor Receptor (EGFR) mutations were significantly associated with WTC exposure type (p = 0.01).

    Conclusions:

    • LLMs can effectively identify a broader population of WTC survivors, including those with genetic information.
    • This approach enhances the capture of underrepresented WTC-exposed populations for further research.
    • Findings underscore the long-term health risks associated with WTC exposure and highlight potential genetic predispositions.