Artificial Intelligence in Cancer Research: Applications and Advancements

The integration of Artificial Intelligence (AI) in the medical field has revolutionized healthcare practices, enabling more accurate, personalized, and efficient diagnosis and treatment of diseases. This paper explores the applications of AI in oncology, focusing on its potential to significantly improve early detection and diagnosis. Comprehensive patient monitoring and treatment planning are also discussed. The efficiency and precision of AI in treatment planning instill confidence in the effectiveness of cancer treatments and reassure us about the future of cancer care. Background: The historical evolution of AI in cancer research, from essential pattern recognition to sophisticated machine learning and deep learning models, is a testament to its progress. These models have demonstrated significant potential in analyzing medical images, such as mammograms and CT scans, to detect tumors at early stages. The development of robotic systems in cancer surgery represents a transformative leap in medical technology. Integrating advanced hardware and AI has empowered these systems to assist in increasingly complex procedures, enhancing precision and improving patient outcomes. AI applications in robotic surgery provide real-time data analysis, help avoid obstacles during surgery, warn of potential complications, and allow surgeons to ‘feel’ the tissues and structures being manipulated. Methods: This study reviewed the use of AI in oncology, searching multiple databases including Wiley, Taylor and Francis, Scopus, and others. Relevant studies were selected based on keywords and inclusion criteria, and a thematic synthesis was conducted to identify trends and research gaps. The credibility of each study was evaluated through a quality assessment, considering methodology, results, and discussion. Results: The integration of AI in cancer diagnosis is revolutionizing the field through two main paths: medical imaging and digital pathology. AI is improving accuracy, efficiency, and personalized diagnosis in both areas. In medical imaging, AI can analyze complex images like mammograms and CT scans, detecting tumors earlier and reducing false positives and negatives. This frees up radiologists to focus on complex cases. In digital pathology, AI can analyze digital images of tissue samples, improving the accuracy and sensitivity of diagnosis. AI is also being developed to detect specific biomarkers, mitosis nuclei, and metastases in various cancers. Overall, the development of AI models is enhancing the range of AI applications in more types of cancers, automating tasks, improving image detection, and unlocking spatial insights. This transformative force will provide more accurate, efficient, and personalized cancer care. Conclusion: The integration of AI in cancer research has shown remarkable promise in improving early detection and diagnosis, patient monitoring, and treatment planning. The development of robotic systems in cancer surgery has enhanced precision and improved patient outcomes. The fusion of AI with digital pathology and medical imaging has revolutionized cancer diagnostics by providing high-resolution images and enhancing diagnostic accuracy. As AI applications continue to evolve, the potential for further advancements in cancer diagnosis and treatment remains substantial, offering hope for the future of cancer research.

کلیدواژه ها:

Artificial Intelligence ، Cancer Research ، Machine Learning ، Robotic Surgery ، Medical imaging