Artificial intelligence in pharmacovigilance - Opportunities and challenges.

Area of Science:

• Pharmacovigilance
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Pharmacovigilance (PV) relies on processing vast amounts of adverse event (AE) data, a complex, time-consuming, and expensive manual process.
• The exponential growth in AE reports presents significant challenges in data management and analysis for stakeholders.
• Artificial intelligence (AI) shows promise in healthcare, particularly in data interpretation, leading to interest in its application within PV.

Purpose of the Study:

• To review the benefits and challenges of implementing AI tools for automating the pharmacovigilance process.
• To assess the scientific, technological, and policy issues surrounding AI adoption in PV.
• To evaluate the maturity of AI for full automation within the Indian healthcare system.

Main Methods:

• Literature review of AI applications in pharmacovigilance.
• Analysis of the PV case processing lifecycle and its susceptibility to automation.
• Examination of AI's potential impact on efficiency, cost, and data interpretation in PV.
• Consideration of human factors and process integration for AI implementation.

Main Results:

• AI can complement and automate routine PV tasks, boosting efficiency and aiding in separating critical data ('needles from haystack').
• Full automation presents challenges, requiring careful consideration of both people and processes.
• A collaborative approach augmenting human expertise with AI technology is recommended.

Conclusions:

• AI offers significant potential to enhance pharmacovigilance efficiency and data analysis.
• Successful AI integration requires a balanced approach, focusing on augmenting human capabilities rather than full substitution.
• Addressing scientific, technological, and policy challenges is essential for realizing AI's full benefits in PV, particularly in the Indian healthcare context.