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The Use of Artificial Intelligence to Predict and Support Medication Adherence in Breast Cancer Patients: A Systematic Review
ABSTRACT
Background:
Oral medications are commonly used in the treatment of breast cancer (BC) patients, despite a high association with non-adherence rates. Since treatment compliance is fundamental to achieving optimal benefit from treatments, finding ways to effectively improve adherence to medication in BC patients is of utmost importance. Artificial Intelligence (AI) is being widely applied to healthcare, and medication adherence is no exception.
Objective:
This review aims to offer an overview of the state-of-the-art contribution of AI to medication non-adherence among BC patients, suggesting future research directions, and highlighting existing gaps.
Methods:
Three databases (PubMed, Embase, and Scopus) were systematically searched to identify studies applying AI to predict or support medication adherence among BC patients. The systematic review was conducted following PRISMA guidelines and was registered in PROSPERO database. The risk of bias and the applicability of the included studies were evaluated using the Prediction Model Risk of Bias Assessment Tool and the Downs and Black's methodological quality scale.
Results:
Seven studies were included in the review: six focused on developing machine learning predictive models, and one described an AI-powered intervention aimed at supporting patient adherence. Performance metrics of the AI models ranged from moderate to high, identifying several predictors that can be grouped in four categories: (1) clinical, disease-, and treatment-related factors; (2) behavioural factors; (3) psychosocial factors; and (4) socioeconomic factors. The only intervention included relied on an AI-powered chatbot, which demonstrated promising results in supporting medication adherence in BC patients. However, all studies showed a high overall risk of bias, so their findings must be interpreted cautiously.
Conclusions:
The implementation of AI in healthcare offers promising support for healthcare professionals by providing innovative tools for predicting, monitoring, and facilitating timely medication adherence among BC patients. However, several critical challenges must be addressed to ensure the clinical utility of such tools, their safety, and adherence to ethical standards. These include the need for rigorous model validation, effective clinical integration, and alignment with ethical and regulatory frameworks.
Citation
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Copyright
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