Robust Clinical Trial Evidence for Healthcare AI
Robust Clinical Trial Evidence for Healthcare AI
Published 5/2025
Duration: 44m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 988 MB
Genre: eLearning | Language: English
Published 5/2025
Duration: 44m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 988 MB
Genre: eLearning | Language: English
Strategic Design and Implementation
What you'll learn
- Equip learners with a comprehensive understanding of how AI tools differ from traditional medical devices and pharmaceuticals in clinical trials. This includes
- Provide learners with the knowledge and tools to design robust clinical trial protocols specifically for AI applications in healthcare. This encompasses selecti
- Educate learners on the ethical considerations, regulatory compliance considerations, and the importance of multidisciplinary collaboration in the development a
- Have fun in the process!
Requirements
- None
Description
As AI continues to transform healthcare, the need for rigorous, transparent, and ethically sound clinical evidence has never been more critical. This course equips professionals with the knowledge and tools necessary to design and implement high-quality clinical trials tailored specifically for AI-based digital tools.
Participants will explore how to integrate multimodal data sources—including EHRs, imaging, and genomics—into clinical trial protocols to evaluate AI tools effectively. The course emphasizes the importance of defining meaningful outcome measures that reflect real clinical impact, while ensuring that both datasets and participant pools are representative of the diverse populations AI is intended to serve.
Learners will be guided through strategies for iterative testing and validation, including phased trial designs and real-world performance monitoring, with a strong focus on maintaining model transparency and performance across different environments and subgroups. The course also addresses the ethical dimensions of AI trials, such as consent, algorithmic bias, and equitable impact, alongside best practices for regulatory compliance and documentation.
Through a multidisciplinary lens, learners will engage with frameworks for collaboration between clinicians, data scientists, and regulators. They will also explore the value of real-world evidence and post-market surveillance in supporting the long-term safety, effectiveness, and trustworthiness of AI tools in clinical settings.
By the end of this course, participants will be prepared to lead or contribute to the development of robust clinical trial strategies for AI tools, ensuring these technologies are safe, effective, and ethically deployed across the healthcare ecosystem.
Who this course is for:
- Professionals in the life sciences
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