NIST recently released the Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST-AI-600-1). This framework was developed to assist organizations in identifying the specific risks posed by generative AI and suggests risk management actions that align with their goals and priorities in response to the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
Driving Innovation in Life Sciences
NIST’s AI Risk Management Framework provides several benefits to life science organizations:
- The framework helps manage risks related to AI in compliance-heavy industries, which is critical for ensuring adherence to FDA and global regulations in life sciences.
- Life sciences firms handle sensitive health data, and the framework provides guidance on maintaining privacy and security in AI applications like drug discovery, diagnostics, and patient care.
- This is notable given that Axendia’s research report: The State of Generative AI in Life Sciences: The Good, The Bad and The Ugly, reveals that 53% of companies are only ‘somewhat prepared’ to manage the data privacy issues associated with generative AI. As a result, many organizations are still in the process of developing or strengthening their data privacy and protection protocols to fully address the nuances introduced by AI technologies.
- The framework highlights the importance of transparency and fairness, ensuring that AI algorithms used in life sciences—such as in clinical trials or medical devices—are explainable, unbiased, and trustworthy. This is essential for upholding ethical AI practices in patient care and clinical decision-making.
- This is essential since our research also shows 56% of companies identified bias and ethical concerns as the top barriers to implementing generative AI in drug discovery. These concerns were also significant in post-market surveillance, where maintaining fairness and ethical standards when using generative AI remains a key challenge.
Life sciences organizations can use the framework to systematically assess and mitigate the risks of AI across R&D, manufacturing, and clinical environments, enhancing trust and safety in AI-driven processes. For example, a pharmaceutical company developing AI models for drug discovery could apply the NIST AI framework to evaluate potential biases in its algorithms, ensure data integrity, and implement safeguards to prevent ethical concerns. By following the framework, the company can conduct regular assessments to monitor AI model performance, address any unintended biases, and ensure transparency, ultimately building trust with regulatory bodies and patients alike.
In related news, another significant step has been taken towards international cooperation on AI governance. The US, UK, and EU have signed on to the Council of Europe’s high-level AI safety treaty. This landmark agreement aims to establish global standards for responsible AI development and use, focusing on safety, transparency, and accountability. As life sciences increasingly adopt AI-driven solutions, such global efforts will help ensure that AI technologies in healthcare are deployed ethically and securely, aligning with frameworks like the NIST AI Risk Management Framework.
In Brief
The framework supports responsible deployment of generative AI, a growing trend in life sciences for areas like drug design, biological modeling, and patient data analysis. By adopting this framework, life sciences organizations can harness the full potential of generative AI while ensuring ethical standards, safety, and trust in their innovations. Additionally, integrating continuous monitoring and validation processes within the framework ensures that AI systems remain compliant with evolving regulations and adapt to emerging ethical challenges, further reinforcing responsible AI deployment.
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.