Abstract
<jats:p>Artificial intelligence (AI) is rapidly reshaping the pharmaceutical industry, supporting activities across discovery, development, manufacturing, quality and regulatory functions. As adoption accelerates, organizations face a significant challenge in how to deploy AI safely, consistently and in compliance with expanding and evolving global regulatory expectations. AI behaves differently from traditional software; its adaptiveness, opacity and reliance on data introduce new risks that affect patient safety, product quality and regulatory trust. At the same time, the external regulatory environment is becoming more complex. The European Union (EU) AI Act1 introduces a comprehensive, cross‑sector risk‑based regulatory model, while the Food and Drug Administration (FDA), European Medicines Agency (EMA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), Association for the Advancement of Medical Instrumentation (AAMI) and International Society for Pharmaceutical Engineering (ISPE) have each released AI‑specific frameworks that set expectations for governance, transparency, explainability, lifecycle assurance and proportionate oversight. These frameworks are robust individually but fragmented collectively, creating uncertainty for quality, regulatory, IT, data science and operational teams who must evaluate AI technologies across diverse GxP environments. This paper, developed by the BioPhorum quality AI risk guidance workstream, addresses that challenge by analyzing the most relevant global AI risk frameworks and synthesizing their shared principles into an industry-backed, recommended harmonized framework, designed specifically for the pharmaceutical industry. The framework aligns AI-specific concepts such as model influence, autonomy, adaptiveness and decision consequence with established GxP risk management practices. It provides a practical, step-by-step approach to classify AI risks consistently, supporting the determination of proportionate controls and integration of AI assurance into existing quality management systems (QMS). Leveraging the BioPhorum recommended, harmonized framework enables organizations to: • Improve consistency of AI risk identification and classification when considering potential AI use cases • Consider appropriate, proportionate and evidence‑based AI validation and oversight commensurate with risk • Accelerate safe and compliant adoption of AI. This unified, implementation-ready model supports innovation while preserving the high standards of safety, efficacy and compliance expected in the pharmaceutical industry.</jats:p>