Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Qin, Pei; Luo, Yanan; Chen, Yiyu; Li, Jingyuan; Xie, Dan; Ye, Ting

doi:10.1515/cclm-2022-0291

“…Numerous research studies have been conducted with the aim of enhancing the accuracy of disease diagnosis and prognosis, encompassing various ailments such as cancer, among other diseases using diverse number of data including metagenomics [17][18]. Figure 1 presents a concise overview of the sequential process involved in developing a diagnostic model for diseases.…”

Section: Current Limitation In Diagnosis Of Autoimmune Diseasesmentioning

confidence: 99%

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Zehrh¹,

Habiba²,

Picco³

et al. 2023

Preprint

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The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

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“…Therefore, more work remains to be done to ensure accurate and timely identification of autoimmune illnesses. The diagnosis and follow-up of some diseases, including autoimmune disorders, have shown significantly improvement during the same period thanks to developments in machine learning [17][18].…”

Section: Of 26mentioning

confidence: 99%

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Zehrh¹,

Habiba²,

Picco³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

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“…For example, as a predictive modeling and early detection, AI could be used to analyze data from a variety of sources, such as electronic health records, genetic information, and environmental data, to predict an individual’s risk of developing cancer and to tailor prevention strategies accordingly [ 13 , 14 , 15 , 16 ]. AI-related applications may reduce screening costs [ 17 ], provide more reliable diagnostics [ 13 , 18 , 19 , 20 ], improve prognostics [ 13 , 19 , 21 , 22 , 23 , 24 , 25 ], and aid in the discovery of new drugs [ 14 , 15 ]. Several areas of cancer care are expected to benefit from AI-related applications, including cancer radiology and clinical oncology [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies sought to anticipate the future of AI use in cancer care through a literature review [ 6 , 9 , 10 , 25 , 33 , 34 , 35 , 36 ]. Overall, most of them focused on specific areas, such as precision medicine [ 6 ], clinical oncology [ 37 ], diagnosis [ 33 ], and cancer target identification [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

Cabral

¹

,

Braga

²

,

Syed-Abdul

³

et al. 2023

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Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.

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Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Cited by 46 publications

References 90 publications

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

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