Artificial Intelligence‐Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation

Although lung cancer remains a global threat to public health, evidenced based advances in screening and prevention hold promise for reducing its impact on mortality. An ongoing challenge facing the clinical and research community are the glaring disparities in access to preventive services faced by ethnically and socioeconomically marginalized groups. In this context, novel approaches are needed to improve research methods and thus bolster our ability to improve outcomes. Artificial intelligence (AI) applications such as machine learning and natural language processing hold promise as catalysts in this process, enhancing speed, accuracy and capability. This perspective will highlight the potential of AI methods as essential tool for growth across the lung cancer diagnostic continuum from screening to diagnosis.

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Publication Details

Journal
Cancer Innovation
Published
2025-06-20
DOI
https://doi.org/10.1002/cai2.70019
Citations
2
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
3.04
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article

Artificial Intelligence‐Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation

Stephen Kuperberg, David C. Christiani
2 citations
Cancer Innovation
Lung Cancer Diagnosis and Treatment
3.04
article

Artificial Intelligence‐Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation

Stephen Kuperberg, David C. Christiani
article en
2 citations

Abstract

Although lung cancer remains a global threat to public health, evidenced based advances in screening and prevention hold promise for reducing its impact on mortality. An ongoing challenge facing the clinical and research community are the glaring disparities in access to preventive services faced by ethnically and socioeconomically marginalized groups. In this context, novel approaches are needed to improve research methods and thus bolster our ability to improve outcomes. Artificial intelligence (AI) applications such as machine learning and natural language processing hold promise as catalysts in this process, enhancing speed, accuracy and capability. This perspective will highlight the potential of AI methods as essential tool for growth across the lung cancer diagnostic continuum from screening to diagnosis.

Cancer InnovationVol. 4(4)
New York City Health and Hospitals Corporation (US), Harvard University (US), Woodhull Medical and Mental Health Center (US), Massachusetts General Hospital (US), New York University (US)
Openalex Percentile: Top 9%
Lung Cancer Diagnosis and Treatment
3.04
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