As a consulting company we have been called into many situations where we have to help our clients with AI and specifically AI compliance in the GxP domain. Implementing AI, and especially Gen AI, in a GxP setting is complicated enough as it is for a variety of reasons:
- Data Privacy and Security Risks: The potential for data breaches and unauthorized access to sensitive patient data is a major concern.
- Algorithmic Bias: AI algorithms can perpetuate biases present in the training data, leading to unfair and discriminatory outcomes.
- Lack of Standardization: The absence of standardized guidelines and regulations for AI development and deployment can hinder innovation and create uncertainty.
- Ethical Implications: The ethical implications of AI, such as the potential for misuse and unintended consequences, raise serious concerns.
- Technical Complexity: Developing and deploying AI solutions at scale, requires specialized technical skills and expertise, which can be a significant barrier for many organizations.
- Dependency on External Data Sources: Reliance on external data sources can introduce risks related to data quality and security.
- Regulatory Compliance: Ensuring compliance with regulatory requirements, such as FDA regulations, can be complex and time-consuming.
- Cost and Resource Constraints: AI initiatives can be resource-intensive, requiring significant investments in technology, talent, and infrastructure.
All of the above are important concerns, however, we are noticing that the two biggest roadblocks that scuttle AI initiatives in GxP settings are as follows:
- Job Displacement and the Internal Politics: Concerns about job losses due to automation and AI-driven technologies. This is no mean roadblock. Uncertainty, doubt and especially fear of being replaced creates an atmosphere where stakeholders are waiting for the solution to show even the slightest failure (which is inevitable in any technology implementation) and then pounce all over it. This has derailed many a Gen AI solution that seldom goes beyond the POC phase.
- Interpretability and Explainability: Understanding the decision-making process of complex AI models can be challenging, especially for regulatory bodies and compliance stakeholders. Here is where GxSpeed personnel have helped demystify and marry the (real) compliance concerns with the (real) risks and arrive at meaningful outcomes.
November 6, 2024
By: Subbu Viswanathan, CISO