Validations: Intended Use, Data Readiness & Bias
Feature Governance: Selection, Encoding, and Drift Susceptibility In the rapidly evolving landscape of pharmaceuticals, the implementation of artificial intelligence (AI) and machine learning (ML) technologies has become increasingly prevalent. However, ensuring compliance with regulatory standards while harnessing the power of AI/ML requires a thorough understanding of model validation, intended use, data readiness, bias consideration, and governance practices. This detailed step-by-step…
Feature Governance: Selection, Encoding, and Drift Susceptibility Feature Governance: Selection, Encoding, and Drift Susceptibility In the growing domain of artificial intelligence (AI) and machine learning (ML) within Good Practice (GxP) frameworks, particularly in pharmaceutical analytics, the validation of models constitutes a challenging yet critical hurdle. Companies must ensure compliance with rigorous regulatory requirements such as those outlined by FDA, EMA,…
Missing Data & Imputation: Guardrails for Regulated Use As Artificial Intelligence (AI) and Machine Learning (ML) technologies increasingly permeate the pharmaceutical landscape, ensuring their compliance with Good Automated Manufacturing Practice (GxP) guidelines becomes paramount. This article serves as a comprehensive guide for pharmaceutical professionals focusing on AI/ML model validation, particularly addressing the critical factors of intended use, data readiness, bias…
Missing Data & Imputation: Guardrails for Regulated Use In the evolving landscape of pharmaceutical analytics and artificial intelligence (AI), understanding the challenges associated with missing data and imputation is critical. As AI and machine learning (ML) gain traction in Good Automated Manufacturing Practice (GxP) environments, adherence to regulatory standards such as FDA, EMA, and MHRA becomes paramount. This article serves…
Outlier Detection Before Model Training Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being integrated into pharmaceutical processes to enhance data-driven decision-making. One critical step in ensuring the efficacy and regulatory compliance of AI/ML models is the detection of outliers prior to model training. This tutorial provides a comprehensive step-by-step guide to outlier detection, focused on regulatory requirements and…
Outlier Detection Before Model Training Outlier Detection Before Model Training In the realm of pharmaceutical analytics, especially within the scope of Artificial Intelligence (AI) and Machine Learning (ML), the processes of validation and governance are paramount. Effective validation ensures that models used in drug development and clinical operations are robust, reliable, and compliant with regulatory standards such as those established…
Ground Truth Management: Versioning and Traceability This article serves as a comprehensive step-by-step tutorial guide on the validation of AI and machine learning (ML) models in Good Practice (GxP) analytics. As regulatory compliance becomes increasingly stringent, understanding the concepts of ground truth management, intended use, data readiness and bias, model verification and validation, as well as drift monitoring and re-validation…
Ground Truth Management: Versioning and Traceability Ground Truth Management: Versioning and Traceability In the realm of pharmaceutical development and clinical operations, effective AI/ML model validation is pivotal. This comprehensive guide provides an in-depth step-by-step tutorial aimed at professionals in pharma, clinical operations, regulatory affairs, and medical affairs. It covers essential elements such as intended use risk, data readiness curation, bias…
Intended Use for AI in GxP: Problem Statements That Survive Audit Intended Use for AI in GxP: Problem Statements That Survive Audit Introduction to AI/ML in GxP Environments The rise of artificial intelligence (AI) and machine learning (ML) in the pharmaceutical sector has created new possibilities for data-driven decision-making. However, implementing AI/ML technologies within Good Practice (GxP) environments brings on…
Data Readiness Checks: Completeness, Consistency, Timeliness Data Readiness Checks: Completeness, Consistency, Timeliness The advent of AI and ML technologies has revolutionized various sectors, including pharmacovigilance, drug discovery, and more. However, the integration of these technologies presents unique challenges, particularly in ensuring data readiness for regulatory compliance. This comprehensive tutorial serves as a step-by-step guide focusing on data readiness checks vital…