Triple-negative breast cancer (TNBC) accounts for 15–20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.
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This research was supported by the Key Research and Development Project of Sichuan Province (2023YFS0171 to JM).
The authors declare no competing interests.
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Guo, J., Hu, J., Zheng, Y. et al. Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer. Br J Cancer (2023). https://doi.org/10.1038/s41416-023-02215-z