Harnessing AI: Revolutionizing Cancer Care and Research
DOI:
https://doi.org/10.58445/rars.845Keywords:
Cancer, Artificial intelligence, Machine learning, Deep learning, Neural Networks, Cancer imaging, Cancer pathology, Literature review, OncologyAbstract
Introduction/Background
Following heart disease, cancer is the second leading cause of death, with approximately 609,820 deaths predicted to occur in the United States in 2023. With this in mind, identifying more sophisticated and efficient methods of diagnosing cancer is crucial. This paper discusses the promising role of artificial intelligence in the field of cancer, focusing on convolutional neural networks and other deep learning models.
Methods
We conduct a literature review, in which peer-reviewed articles in BioMed Central, Pubmed, Google Scholar, Nature, Science Direct, and National Cancer Institute (NCI) databases are analyzed, focusing on publications between 2016 and 2023. Through the use of the developed inclusion and exclusion criteria, the articles utilized in this paper are narrowed down to 101 articles. Articles are only selected if published within the last seven years and contain important keywords, such as “artificial intelligence”, “cancer”, and “machine learning”.
Results
AI models have proven effective in the early diagnosis of many cancers through imaging and pathology, including lung, breast, gastric, and prostate cancer. Indeed, deep learning models such as convolutional neural networks have proven to be highly accurate in their validation test sets, in which several reached high accuracies comparable to expert physicians.
Discussion/Future Work
As cancer continues to have a grave impact on individuals worldwide, it is crucial to develop more efficient methods for cancer diagnoses. In the near future, we must work towards addressing the challenges standing in between implementing AI into clinical practice. These challenges include resolving both legal and ethical concerns, biases, availability of training datasets, and interpretability.
Conclusion
The high accuracy of several artificial intelligence models in recent studies demonstrate their potential to aid physicians. The articles selected in this review discuss the achievements, challenges, and future of such algorithms within the field of cancer.
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