How to Approach Success with Evolving AI Regulatory Environment

The life sciences sector adapts to AI for better access to care, improved results and lower costs, but it must adopt governance and regulatory policies.

Fremont, CA: Artificial intelligence (AI) and machine learning are being widely adopted in various sectors. One of those sectors is the life sciences sector, where AI is used for research, diagnosis, treatments and prevention. To benefit from AI, policymakers must focus on legislation to tighten controls because of the complexity of AI. Data privacy, bias, verification and accountability are some of the issues with AI in life sciences.

To benefit from AI, companies must adopt these three approaches:

Governance and regulation

Development, regulatory approval, and continuing compliance are distinct obstacles for AI as a smart decision-making tool. To begin, AI solutions must be integrated into the current quality and documentation procedures. A wide range of stakeholders with appropriate regulatory experience, which includes IT, security, analytics, and legal and risk specialists, may be required for an AI governance model. Compared to traditional product development, these stakeholders may need to become involved with the project earlier and more frequently.

Data management

For training, analytical evaluation, and continual evolution, AI solutions need massive volumes of data. Data collection and use operations and data security management must be adaptable and further scalable. Data testing and analysis methods and techniques must give auditable evidence to verify safety assurances and testing findings. Preparing and labelling AI data for training or clinical sets, is still a time-consuming and expensive procedure.

Reporting and tracking mechanisms

Effective AI solutions are adaptable, needing a continuous examination of the data and facts upon which they are based and the metrics and KPIs that they are intended to attain. Because AI is adaptive, it requires agile software development procedures and supporting processes for safety and audit, risk management, quality assurance, and documentation. Existing reporting and tracking techniques must be adapted to the dynamic, ongoing activities of AI.

AI can provide many benefits, including more access to care, improved results, and lower costs. Its widespread adoption and deployment necessitate changes in operational and governance frameworks and monitoring and even engaging in the regulatory process.

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