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Anticipating Regulatory Compliance for Artificial Intelligence in Life Sciences

Anticipating Regulatory Compliance for Artificial Intelligence in Life Sciences

Artificial intelligence (AI) and machine learning have transformative powers; learn how to adopt new technologies while the industry awaits regulatory requirements for their responsible use.

Life sciences organizations depend on guidance and best practices for developing and validating technology solutions. However, there are no currently enforced regulatory guidelines for the use of AI.

Explore the progress of regulatory guidelines—read the white paper > > >

The Evolution of Regulatory Considerations

As regulatory requirements evolve for the use of AI and machine learning, we’re seeing guiding principles from the likes of the U.S. Food and Drug Administration (FDA), Health Canada, and the Medicines and Healthcare products Regulatory Agency (MHRA) in the United Kingdom. The vision is that these principles will be used to: (1) adopt proven practices from other sectors, (2) tailor those practices to life sciences, and (3) create practices that are specific to medical technology and the healthcare sector.

See who’s doing what to support the use of AI—download the white paper > > >

AI Governance and Risk Management

The U.S. and European Union (EU) align on the concept of a risk-based approach to AI in the life sciences industry and agree on key principles for trustworthy AI:

  • Accuracy
  • Robustness
  • Data privacy
  • Safety and security 
  • Non-discrimination
  • Accountability and transparency
  • Explainability and interpretability

Review of risks and controls is a continuous process. This white paper explains why human knowledge and expertise is necessary for reviewing AI risk based on intended use and output and to discern whether AI is behaving as expected.

Learn more about AI risks and benefits—get the white paper > > >

Demonstrating the Value of AI

Calculating the value of AI for your organization might start with automation—having AI perform the tedious or repetitive tasks for day-to-day business. Every use case has its unique factors, but the benefits of AI can be demonstrated by:

  • Efficiency and speed
  • Accuracy and reliability
  • Scalability and flexibility
  • Cost-effectiveness
  • Discovery and insight

Contact USDM today to learn how our AI framework and industry best practices can help your organization move from proofs of concept to innovative AI solutions.

Read through interim best practices for using AI in your life sciences organization—get the white paper > > >

Contributors to this white paper:

John Petrakis, VP of Cloud Assurance
Michelle Gardner, Senior Researcher and Writer
David Blewitt, VP of Cloud Compliance
Lisa Om, VP of Marketing and Communications
Dan Oriold, Director of Product Management, Cloud Assurance

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