• Early Cancer Detection Using Artificial Intelligence: A Review of Current Approaches
  • Hasti Jahanbaz,1,*
    1. Shahid Gholamreza Ebrahimi High School


  • Introduction: Early detection of cancer significantly improves patient survival and treatment outcomes. Traditional methods, including imaging and lab tests, can be limited by cost, time, and dependence on specialist expertise. Artificial intelligence (AI) offers a promising solution by analyzing large biomedical datasets to detect early signs of cancer that may be difficult for humans to identify.
  • Methods: This study reviews current AI techniques applied in cancer detection. Focus is on machine learning and deep learning algorithms, such as convolutional neural networks for medical imaging, and predictive models for genetic and clinical data. Peer-reviewed articles and recent research studies were analyzed to summarize the effectiveness, advantages, and challenges of AI-based approaches.
  • Results: AI models demonstrate high accuracy in detecting tumors from radiology images, classifying genetic mutations, and predicting cancer risk in early stages. Benefits include faster diagnosis, consistent results, and the potential to expand access to healthcare in resource-limited areas. Challenges include the need for high-quality training data, ethical considerations, and interdisciplinary collaboration between computer scientists and medical professionals.
  • Conclusion: Integrating AI into cancer prevention, diagnosis, and treatment can transform healthcare by assisting physicians, reducing diagnostic errors, and enabling earlier interventions. Continued research and development are crucial to improve accessibility and reliability of AI tools for future medical systems.
  • Keywords: Cancer, Artificial Intelligence, Early Detection, Machine Learning, Biomedical Informatics