ARTIFICIAL INTELLIGENCE IS CHANGING FACE OF PHARMACOVIGILANCE

Approved

Relevance and Originality

This paper addresses a highly relevant and critical issue in healthcare—the safety of pharmaceuticals—making it significant for stakeholders in the medical and regulatory fields. The incorporation of artificial intelligence (AI) and machine learning (ML) into pharmacovigilance systems is an innovative angle that reflects current trends in the industry. However, while the topic is original, the review could be enhanced by including unique case studies or examples where AI and ML have been successfully integrated into pharmacovigilance, showcasing tangible outcomes. Highlighting specific innovations or emerging technologies within this space would also strengthen its originality.

Methodology

The paper lacks a clear description of the methodology used for gathering data or selecting studies for review. It would benefit from detailing whether it is based on a systematic literature review, meta-analysis, or expert opinion. This clarity would establish a foundation for the findings presented. Additionally, discussing the selection criteria for included studies or data sources would enhance the transparency and reliability of the review. Employing a structured approach to analyze the impact of AI and ML on pharmacovigilance outcomes, perhaps through comparative analysis, would further enrich the methodology.

Validity & Reliability

While the paper makes compelling arguments regarding the potential benefits of AI and ML in pharmacovigilance, the validity of these claims would be bolstered by including empirical data or statistical evidence. Discussing the limitations of current data sources and the biases that might arise from underreporting or data quality issues would provide a more nuanced understanding of the challenges faced in the field. Additionally, integrating perspectives from regulatory authorities or pharmaceutical companies could lend reliability to the findings and ensure a balanced viewpoint.

Clarity and Structure

The review is well-structured, with clear headings that guide the reader through the evolving landscape of pharmacovigilance. However, the clarity could be improved by simplifying complex sentences and avoiding jargon where possible. Including bullet points or tables to summarize key findings or challenges would enhance readability. Additionally, defining technical terms and acronyms at their first occurrence would help make the paper accessible to a broader audience, including those who may not be specialists in pharmacovigilance.

Result Analysis

The analysis of the results is insightful but could be enriched by providing specific data or case studies demonstrating the practical applications of AI and ML in pharmacovigilance. Including quantitative metrics on how these technologies have improved ADR detection rates or reduced reporting times would substantiate the claims made. Furthermore, discussing potential future research directions or frameworks for implementing AI in pharmacovigilance could offer a forward-looking perspective, helping to contextualize the current challenges within a broader vision for the field.