PDA Technical Glossary

AI driven identification of potential drug side effects and adverse events, supporting risk mitigation.

The use of AI to enhance the efficiency and effectiveness of clinical trials, including streamlining patient recruitment, monitoring electronic case report forms (eCRF), and optimizing data analysis.

The assurance that the data remains complete, consistent, accurate, trustworthy, and reliable throughout its life cycle. Maintaining data integrity is crucial for ensuring the validity of AI-driven insights, supporting regulatory compliance, and enabling reliable decision-making in pharmaceutical manufacturing.

The protection of patient and healthcare data from unauthorized access, use, disclosure, disruption, modification, or destruction.

The process of identifying and designing new medications, often utilizing AI for virtual screening, target identification, and compound optimization and repurposing.

Human-in-the-Loop (HITL) is a capability and role whereby qualified personnel can meaningfully intervene within the system’s decision cycle during operation or oversight activities or to enhance trust and continuous improvement. These actions are in place to address uncertainty and limitations, override or adjust outputs, and provide feedback that supports continuous performance assurance. Professionals can actively guide, review, and verify the AI output. HITL is applied in a risk-based manner, with the level and timing of oversight, controls, and documentation proportionate to the system’s intended use and risk and evaluated on the performance of the human–AI team, not the model alone.

The science and activities related to the detection, assessment, understanding, and prevention of adverse effects of drugs.

An approach to medical treatment and healthcare that customizes interventions based on the individual patient’s characteristics, often leveraging AI for personalized diagnostics and tailored treatment plans.

Clinical evidence derived from analyzing real-world data, often collected outside the constraints of randomized clinical trials.

Ensuring AI applications in pharmaceuticals adhere to industry regulations, protecting patient safety and maintaining data privacy.

The use of software robots or "bots" to automate routine and repetitive tasks, often used in administrative processes (e.g., small chat bubbles that appear when you enter a website and offer support or guidance). In pharmaceutical applications, RPA may be used to automate data entry and documentation processes.

The application of AI to enhance efficiency, stability, and visibility across the pharmaceutical supply chains, from manufacturing to distribution. An early application of AI was to calculate the most effective route of delivery to several destinations to enable products to be in transit for as short a time as possible and thus support product stability, cost, and sustainability.