Artificial Intelligence and Machine Learning for Drug Development – FDA.gov
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Artificial Intelligence (AI) and Machine Learning (ML) can be described as a branch of computer science, statistics, and engineering that uses algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is considered a subset of AI that allows models to be developed by training algorithms through analysis of data, without models being explicitly programmed.
FDA recognizes the increased use of AI/ML throughout the drug development life cycle and across a range of therapeutic areas. In fact, FDA has seen a significant increase in the number of drug and biologic application submissions using AI/ML components over the past few years, with more than 100 submissions reported in 2021. These submissions traverse the landscape of drug development — from drug discovery and clinical research to postmarket safety surveillance and advanced pharmaceutical manufacturing.
Additionally, AI/ML is increasingly integrated in areas where FDA is actively engaged, including Digital Health Technologies (DHTs), and Real-World Data (RWD) analytics.
FDA is committed to ensuring that drugs are safe and effective while facilitating innovations in their development. As with any innovation, AI/ML creates opportunities and new and unique challenges. To meet these challenges, FDA has accelerated its efforts to create an agile regulatory ecosystem that can facilitate innovation while safeguarding public health.
As part of this effort, FDA’s Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), issued an initial discussion paper to communicate with a range of stakeholders and to explore relevant considerations for the use of AI/ML in the development of drugs and biological products. The agency will continue to solicit feedback as it advances regulatory science in this area.
AI/ML will undoubtedly play a critical role in drug development, and FDA plans to develop and adopt a flexible risk-based regulatory framework that promotes innovation and protects patient safety.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
Artificial Intelligence (AI) and Machine Learning (ML) can be described as a branch of computer science, statistics, and engineering that uses algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is considered a subset of AI that allows models to be developed by training algorithms through analysis of data, without models being explicitly programmed.
FDA recognizes the increased use of AI/ML throughout the drug development life cycle and across a range of therapeutic areas. In fact, FDA has seen a significant increase in the number of drug and biologic application submissions using AI/ML components over the past few years, with more than 100 submissions reported in 2021. These submissions traverse the landscape of drug development — from drug discovery and clinical research to postmarket safety surveillance and advanced pharmaceutical manufacturing.
Additionally, AI/ML is increasingly integrated in areas where FDA is actively engaged, including Digital Health Technologies (DHTs), and Real-World Data (RWD) analytics.
FDA is committed to ensuring that drugs are safe and effective while facilitating innovations in their development. As with any innovation, AI/ML creates opportunities and new and unique challenges. To meet these challenges, FDA has accelerated its efforts to create an agile regulatory ecosystem that can facilitate innovation while safeguarding public health.
As part of this effort, FDA’s Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), issued an initial discussion paper to communicate with a range of stakeholders and to explore relevant considerations for the use of AI/ML in the development of drugs and biological products. The agency will continue to solicit feedback as it advances regulatory science in this area.
AI/ML will undoubtedly play a critical role in drug development, and FDA plans to develop and adopt a flexible risk-based regulatory framework that promotes innovation and protects patient safety.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
