ARTIFICIAL INTELLIGENCE IS CHANGING FACE OF PHARMACOVIGILANCE

Approved

Relevance and Originality

The research article is highly relevant in the context of ongoing discussions about drug safety and patient care. It addresses significant concerns regarding adverse drug reactions (ADRs) and emphasizes the importance of pharmacovigilance in the modern healthcare landscape. The incorporation of artificial intelligence (AI) and machine learning (ML) represents an original contribution, as these technologies are increasingly recognized for their potential to enhance PV systems. However, further exploration of unique case studies or innovative applications of AI in this field could enhance the article's originality and depth.

Methodology

The review outlines a broad exploration of pharmacovigilance but does not clearly specify the methodology used to gather and analyze the literature. A more explicit discussion on the criteria for selecting studies, databases searched, and any systematic approaches used in the review process would provide greater transparency and rigor. Incorporating meta-analyses or case studies could further substantiate the claims made regarding the effectiveness of AI and ML in PV.

Validity & Reliability

The findings presented in the article are based on current literature; however, the validity could be improved with a more thorough examination of the studies referenced. Evaluating the quality of the data sources and the consistency of the findings across various studies would enhance the reliability of the conclusions drawn. Furthermore, discussing potential biases in the literature reviewed, such as publication bias or the influence of regulatory bodies, would add depth to the assessment of validity.

Clarity and Structure

The article is well-structured, with clear sections dedicated to different aspects of pharmacovigilance, including technology integration and challenges. However, certain sections could benefit from more concise language to improve readability. Including visual aids like charts or tables to summarize the key findings and trends in ADR reporting could enhance clarity. A more focused conclusion that encapsulates the main findings and recommendations would strengthen the overall structure of the article.

Result Analysis

While the review discusses the benefits and challenges of AI and ML in pharmacovigilance, it lacks detailed analysis or specific examples of how these technologies have improved drug safety outcomes. Providing quantitative data, such as reductions in reporting time or increased detection rates of ADRs, would substantiate the claims made. Additionally, exploring real-world applications and case studies where AI has been successfully implemented in PV would enrich the result analysis and demonstrate the practical implications of the findings discussed.