Subgroups – 1.3 | AI Innovators & Developers – AIiHC ORG

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1.3 | AI Innovators & Developers

Public Stakeholder connect

Public Stakeholder connect

Active 3 months ago

Purpose: For data scientists, AI engineers, and software developers to discuss the technical,... View more

Public Stakeholder connect

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Group Description

Purpose: For data scientists, AI engineers, and software developers to discuss the technical, scientific, and developmental aspects of creating, refining, and maintaining AI models and software for healthcare.

Description: This group is the primary forum for those directly involved in the coding, modeling, and scientific experimentation behind AI technologies for healthcare applications. It’s where the “builders” of AI come to discuss their craft.

Intended Use: For sharing technical challenges, discussing new algorithms, collaboratively solving coding problems, exploring model architectures, and addressing data-related issues specific to AI model development in healthcare.

Limitations: No Commercial Sales/Promotion: Strictly non-commercial.
No High-Level IT Infrastructure Management: Discussions about server architecture, network security, or broader system integration (e.g., configuring EHR APIs for data flow) belong in “IT & Infrastructure Management”

No Academic Collaboration Planning: Specific discussions about joint research projects, grant applications, or academic curricula go to “Academic & Research Collaboration Hub” (5.0).
No General Clinical Discussions: Focus is on the technical AI, not clinical practice.

Key Activities:
Technical discussions on AI model architectures, training methodologies, and validation techniques.
Sharing insights on handling healthcare data challenges for model development (e.g., bias in datasets, data augmentation).
Peer review of code, algorithms, and development approaches.
Discussions on AI development tools, libraries, and frameworks.

Potential Users: AI Innovator/Developer, Academic/Researcher (when focusing on technical development).

Possible Discussions: “Comparing different deep learning architectures for medical image analysis tasks,” “Challenges of de-identifying sensitive patient data for AI model training,” “Best practices for reproducible AI research in healthcare development environments.”
Other Important Notes: This group is a highly technical space. Participants should have a foundational understanding of AI/ML concepts.

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