Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer

Area of Science:

• Oncology
• Medical Informatics
• Computational Biology

Background:

• Triple-negative breast cancer (TNBC) represents 15-20% of invasive breast cancers.
• TNBC is characterized by aggressive behavior, lack of targeted therapies, and high recurrence rates, leading to poor prognoses.
• Traditional treatment approaches for TNBC are limited due to its complex nature.

Purpose of the Study:

• To review the fundamental principles of artificial intelligence (AI).
• To summarize the diverse applications of AI, particularly machine learning, in TNBC research and clinical practice.
• To offer novel insights and a theoretical framework for advancing TNBC diagnosis and treatment.

Main Methods:

• Review of current literature on AI and machine learning in oncology.
• Analysis of AI applications across the TNBC patient journey, from screening to prognosis.
• Synthesis of findings to identify key areas for AI integration in TNBC management.

Main Results:

• AI, especially machine learning, is increasingly utilized in TNBC for early screening and diagnosis.
• AI facilitates the identification of TNBC molecular subtypes, enabling personalized treatment strategies.
• Machine learning models show promise in predicting patient prognosis and treatment response in TNBC.

Conclusions:

• AI offers significant potential to overcome the challenges associated with treating triple-negative breast cancer.
• The integration of AI into clinical workflows can enhance diagnostic accuracy and treatment efficacy for TNBC.
• Further research and development in AI are crucial for improving outcomes for TNBC patients.