You are very welcome @aashkapatel !

I’m currently building AI-ready clinical data infrastructure for underrepresented populations, I’ve published it here on Manifund and I would appreciate to have your feedback on that 😊

Thank you @Angel_b !

About scalability across countries and healthcare systems:

That's one of the key questions we're trying to answer. Our goal is not to create a Kinshasa-specific tool, but to identify the minimum set of structured data-capture practices that can be integrated into existing clinical workflows in resource-constrained settings. While healthcare systems differ, many facilities face similar challenges: paper-based records, limited connectivity, fragmented reporting requirements, and high clinical workloads. If we can demonstrate that a lightweight approach works in Kinshasa without requiring major infrastructure investments, it could provide a framework that can be adapted to other LMIC contexts rather than a one-size-fits-all solution.

On whether the main barrier is technology or workflow adoption:

Our hypothesis is that the primary challenge is not technology itself, but workflow integration. Many digital health initiatives introduce new tools that require additional effort from already overburdened healthcare staff, which often limits adoption. Clinical data is frequently generated but remains trapped in paper records, free-text notes, or disconnected systems.

We believe that improving representation in AI-relevant datasets depends on making structured data capture a natural by-product of routine care rather than an additional task. If that hypothesis is correct, relatively modest investments in workflow design and data infrastructure could have a greater long-term impact than investing solely in downstream AI models trained on incomplete datasets.

This is a timely and ambitious project that addresses one of the most important governance challenges in the AI era.

As someone interested in digital health, I see significant value in this work. Making AI systems more transparent and easier to understand can help patients, healthcare professionals, and organizations make safer, more informed choices, while strengthening trust and accountability in digital health. Good luck !

This is a compelling and relevant project. The integration of hormonal, neuroinflammatory, structural brain, and cognitive markers is innovative and could help move the field toward more personalized and preventive approaches to Alzheimer's disease risk assessment. Good luck !

I really like your approach on this matter. As someone who is interested in health tech, the focus on energy-bounded constraints (e.g., battery, torque) mirrors real-world healthcare resource limitations, offering foundational insights for future safety protocols in multi-agent medical systems. Well-executed.

This is a great initiative. This work provides a valuable framework for testing auditing robustness against strategically evasive model behaviors.

I think that the red-team approach can also directly address systemic risks in health AI systems where models might conceal unsafe behaviors while maintaining operational functionality which is a critical consideration for real-world clinical safety.

This is brilliant work, exactly the kind of practical, human-centered safety testing we need in health tech too. Your paired-scenario approach (unsafe vs. safe lookalikes) mirrors how we balance clinical efficiency with patient risk in AI-driven care.

By pinpointing where AI oversteps without becoming over-cautious, you’re building the trust infrastructure we desperately need for safe, useful AI systems. Keep pushing this frontier, every safe payment decision is a step toward healthier, more resilient systems.