Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.
By Shigao Huang, Simon James Fong, Qi Zhao. Cancer Letters, 2019
513 citations recorded by the source corpus.
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/9c51ef6b597ecce74173ac33e9bce0656278672d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yogesh Kumar
- Ruchika Singla
- Apeksha Koul
- Mohamad Forouzanfar
- Shiva Maleki Varnosfaderani
- Muhammad Fazal Ijaz
In Health Informatics
In Radiology, Nuclear Medicine and Imaging
- Yogesh Kumar
- Ruchika Singla
- Apeksha Koul
- Mohamad Forouzanfar
- Shiva Maleki Varnosfaderani
- Muhammad Fazal Ijaz
Citations in the corpus
- Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, background): “These models need to be validated in a clinical setting to assist the medical practitioner in affirming the diagnosis verdicts (Huang et al. 2020).”
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “Moreover, AI’s role in prognosis is equally transformative [15,16].”