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Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes
SIMPLE SUMMARY: In this comprehensive review, we aimed to summarize the advances made by artificial intelligence in the field of lung cancer screening, diagnosis, and management. We now understand the utility of AI as a tool that can supplement physicians to improve the quality of care provided, whi...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650618/ https://www.ncbi.nlm.nih.gov/pubmed/37958411 http://dx.doi.org/10.3390/cancers15215236 |
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author | Gandhi, Zainab Gurram, Priyatham Amgai, Birendra Lekkala, Sai Prasanna Lokhandwala, Alifya Manne, Suvidha Mohammed, Adil Koshiya, Hiren Dewaswala, Nakeya Desai, Rupak Bhopalwala, Huzaifa Ganti, Shyam Surani, Salim |
author_facet | Gandhi, Zainab Gurram, Priyatham Amgai, Birendra Lekkala, Sai Prasanna Lokhandwala, Alifya Manne, Suvidha Mohammed, Adil Koshiya, Hiren Dewaswala, Nakeya Desai, Rupak Bhopalwala, Huzaifa Ganti, Shyam Surani, Salim |
author_sort | Gandhi, Zainab |
collection | PubMed |
description | SIMPLE SUMMARY: In this comprehensive review, we aimed to summarize the advances made by artificial intelligence in the field of lung cancer screening, diagnosis, and management. We now understand the utility of AI as a tool that can supplement physicians to improve the quality of care provided, which is the core message of this review, along with the relevant literature supporting the advances. ABSTRACT: Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients. |
format | Online Article Text |
id | pubmed-10650618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106506182023-10-31 Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes Gandhi, Zainab Gurram, Priyatham Amgai, Birendra Lekkala, Sai Prasanna Lokhandwala, Alifya Manne, Suvidha Mohammed, Adil Koshiya, Hiren Dewaswala, Nakeya Desai, Rupak Bhopalwala, Huzaifa Ganti, Shyam Surani, Salim Cancers (Basel) Review SIMPLE SUMMARY: In this comprehensive review, we aimed to summarize the advances made by artificial intelligence in the field of lung cancer screening, diagnosis, and management. We now understand the utility of AI as a tool that can supplement physicians to improve the quality of care provided, which is the core message of this review, along with the relevant literature supporting the advances. ABSTRACT: Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients. MDPI 2023-10-31 /pmc/articles/PMC10650618/ /pubmed/37958411 http://dx.doi.org/10.3390/cancers15215236 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Gandhi, Zainab Gurram, Priyatham Amgai, Birendra Lekkala, Sai Prasanna Lokhandwala, Alifya Manne, Suvidha Mohammed, Adil Koshiya, Hiren Dewaswala, Nakeya Desai, Rupak Bhopalwala, Huzaifa Ganti, Shyam Surani, Salim Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes |
title | Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes |
title_full | Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes |
title_fullStr | Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes |
title_full_unstemmed | Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes |
title_short | Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes |
title_sort | artificial intelligence and lung cancer: impact on improving patient outcomes |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650618/ https://www.ncbi.nlm.nih.gov/pubmed/37958411 http://dx.doi.org/10.3390/cancers15215236 |
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