Trustworthy AI Governance and Validation for Healthcare and Pharmaceutical Systems

Abstract

Artificial intelligence (AI) can be integrated into healthcare and pharmaceutical systems to transform clinical decision-making and drug development, as well as patient care. Nevertheless, the use of AI in such stakes areas requires a sound system of governance and validation in order to maintain safety, reliability and adherence to regulatory measures. This essay offers a comprehensive analysis of the most prominent principles and issues related to reliable AI in healthcare and pharmaceuticals, with a specific focus on the governance model, regulatory adherence, data integrity and models validation procedures. The article discusses the ethical and social consequences of the use of AI such as equity, openness, and the necessity to have human supervision. It further talks about the changing regulatory environment, the need to have constant monitoring, and the multidisciplinary approach in making sure that the AI systems are dependable and safe throughout their life cycle. Good Machine Learning Practices (GMLP), explainable artificial intelligence (XAI) and sound validation strategies are suggested as key ingredients of a credible AI framework. Lastly, the paper has proposed the directions of research in future and best practices to reinforce AI accountability and validation measures in these areas.