Artificial Intelligence in Personalized Breast Cancer Drug Safety: From Preclinical Toxicology to Clinical Risk Management.

PURPOSE: The artificial intelligence (AI) implementation in personalized medicine has transformed drug safety, especially in breast cancer treatment. The importance of the need to treat breast cancer individually is acute as the disorder is heterogeneous and reacts differently to the use of chemotherapeutic agents.

METHODS

Use of AI technologies including machine learning algorithms, deep learning, and predictive analytics can be used to acknowledge the presence of any possible toxicological risks in the early drug development stages to enhance efficacy and safety of therapies.

FINDINGS: The strategies can be used to tailor treatment according to the genetic, molecular, and clinical features of a patient and minimize adverse drug reactions and maximize outcomes. The promise in the application of AI to predict treatment responses, optimize drug dosages, and ensure long-term safety through the combination of clinical trials, patient records, and real-world evidence has been high.

IMPLICATIONS: Throughout this review, it has been shown that AI can transform preclinical toxicology, clinical trial design, and postmarketing surveillance and overcome challenges and opportunities in the expanding area of drug development in breast cancer https://clinicaltrials.gov/.