Use of Nanotechnology and Artificial Intelligence to Improve Predictive Lung Cancer Detection in People Over 60 Years of Age A Systematic Literature Mapping
Adrián H Tozzi
ABSTRACT
Context: Lung cancer represents a major global health challenge, with early detection being crucial for improving treatment and patient outcomes, especially among those over 60 years of age. Emerging technologies such as nanotechnology and artificial intelligence offer promising approaches to improve diagnostic capabilities and prognostic accuracy.
Objectives: This systematic literature mapping aims to comprehensively review existing research on the integration of nanotechnology and artificial intelligence in the early detection of lung cancer in elderly populations. It seeks to identify trends, gaps, and advances in this interdisciplinary field, exploring both the opportunities and challenges these technologies present.
Methods: A systematic search was conducted in major electronic databases, applying predefined inclusion and exclusion criteria. Selected studies were critically appraised for their contribution to the use of nanotechnology and artificial intelligence in lung cancer diagnosis. Finally, they are validated against the research questions.
Results: The study highlighted the growing use of nanotechnology and artificial intelligence in lung cancer detection, including techniques for detecting sensitive biomarkers and enhancing images. Artificial intelligence algorithms were also used for predictive modeling and clinical decision support, reflecting the ongoing evolution of research in this dynamic field.
Conclusions: The innovative potential of combining nanotechnology and artificial intelligence for detecting lung cancer in older adults is highlighted. Future research will seek to expand these applications to other types of cancer and explore new technologies to improve diagnostic accuracy and clinical outcomes, driving the need for innovation in the medical field.
