โก Quick Summary
This review highlights the transformative role of artificial intelligence (AI) in enhancing lung cancer diagnosis and personalized treatment. By leveraging large-scale clinical and imaging datasets, AI tools show promise in improving patient outcomes through early detection and optimized therapeutic decisions.
๐ Key Details
- ๐ Focus: Lung cancer diagnosis and treatment
- ๐งฉ Technologies: Machine learning, deep neural networks, radiomics
- โ๏ธ Applications: Imaging, histopathology, biomarker analysis, multi-omic data integration
- ๐ Key Metrics: Enhanced diagnostic accuracy, risk stratification, molecular profiling
๐ Key Takeaways
- ๐ Lung cancer remains the leading cause of cancer mortality worldwide.
- ๐ก AI advancements offer new avenues for early diagnosis and personalized treatment.
- ๐ AI-based models demonstrate promising performance in early detection and outcome prediction.
- ๐ Challenges include model heterogeneity and generalizability issues.
- โ๏ธ Ethical concerns regarding transparency and clinical accountability must be addressed.
- ๐ค Multicenter collaborations and regulatory frameworks are essential for successful AI integration.
- ๐ฎ Future directions include further validation and standardization of AI tools.
๐ Background
Lung cancer continues to pose a significant challenge in oncology, with survival rates remaining suboptimal due to late-stage diagnoses and the inherent heterogeneity of tumors. Traditional diagnostic methods often fall short in identifying critical patterns that could lead to earlier intervention. The integration of artificial intelligence into lung cancer care presents a promising solution to these longstanding issues.
๐๏ธ Study
This review synthesizes current evidence on the applications of AI in lung cancer care, focusing on its role in early diagnosis and personalized treatment. The authors explore recent developments in AI-driven approaches, including machine learning and deep neural networks, which are applied to various domains such as imaging and biomarker analysis.
๐ Results
AI-based models have shown promising performance in several key areas: early detection of lung cancer, risk stratification, and molecular profiling, including the assessment of biomarkers like PD-L1 and EGFR. These advancements indicate a significant potential for AI to enhance diagnostic accuracy and optimize therapeutic decisions, ultimately leading to improved patient outcomes.
๐ Impact and Implications
The implications of AI in lung cancer care are profound. By improving diagnostic accuracy and enabling personalized treatment strategies, AI tools can significantly enhance patient outcomes. However, the successful integration of these technologies into clinical workflows will require addressing challenges such as model validation, standardization, and ethical considerations. The future of lung cancer management could be revolutionized by these advancements, paving the way for better survival rates and quality of life for patients.
๐ฎ Conclusion
The review underscores the transformative potential of artificial intelligence in lung cancer diagnosis and treatment. As AI technologies continue to evolve, they hold the promise of delivering more accurate and personalized care to patients. Ongoing research and collaboration will be crucial in overcoming existing challenges and ensuring the successful adoption of AI in clinical practice.
๐ฌ Your comments
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The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment.
Abstract
Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption.
Author: [‘Ayasa Y’, ‘Alajrami D’, ‘Idkedek M’, ‘Tahayneh K’, ‘Akar FA’]
Journal: Int J Mol Sci
Citation: Ayasa Y, et al. The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment. The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment. 2025; 26:(unknown pages). doi: 10.3390/ijms26178472