Understanding the context-of-use for each AI shade is crucial to address biases, ensure transparency, and enhance decision-making processes within regulatory frameworks. In this article, the authors emphasize the need for tailored regulatory measures to accommodate AI’s diverse roles, ensuring AI enhances rather than complicates regulatory processes.
Understanding the context-of-use for each AI shade is crucial to address biases, ensure transparency, and enhance decision-making processes within regulatory frameworks. In this article, the authors emphasize the need for tailored regulatory measures to accommodate AI’s diverse roles, ensuring AI enhances rather than complicates regulatory processes.
Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations. Originally published in The AAPS Journal (2023) 25:70
Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations. Originally published in The AAPS Journal (2023) 25:70
In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients.
In this manuscript, we demonstrate the recent advances and predictive accuracies in AI- and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients.
Specific benchmarking practices, the use of Explainable AI and a well-developed technology roadmap are critical success factors to effectively onboard AI/ML for drug development.
Specific benchmarking practices, the use of Explainable AI and a well-developed technology roadmap are critical success factors to effectively onboard AI/ML for drug development.