Reimagining Patient Safety with Explainable AI: Insights from Pharmacovigilance Research

The rapid implementation of the notion of artificial intelligence (AI) in the medical field has provided certain new opportunities to enhance the security of patients, particularly, the field of pharmacovigilance. However, despite the proven good predictive performance of the developed machine learning models and deep learning models in adverse drug reaction (ADR) identification, the low interpretability that is associated with them makes them extremely troublesome in terms of clinical trust, regulation, and ethics regarding their usage. The introduced concept of patient safety will be recreated in the given paper to examine the explanation of explainable artificial intelligence (XAI) regarding the existing studies of pharmacovigilance. We revisit the capability of explainability frameworks such as feature attribution, rule-based modeling, attention visualization and model-agnostic interpretability framework to simplify the process of ADR signal detection, causality assessment and risk stratification by making them transparent. Clinicians can be more accountable with XAI, and their integrations can be increased, and more evidence-based knowledge can be employed to make decisions since the outputs of the algorithms are perceived via a clinical rationale and correspond to regulatory specifications. We also argue about the issue of methodology like bias elimination, heterogeneity of data and compromise between the complexity of the model and interpretation. In conclusion, we arrive at the conclusion that the issues of AI-based pharmacovigilance systems that explainability is one of the core challenges of a technical addition to the necessity of creating responsible and patient-centered care.