A really interesting and timely project. Most of the genetic data out there for clinical research is that of the White Western Europeans; it doesn't contain the DNA from other parts of the world. Hence, I agree with your point that populations in low and middle-income countries are often missing from the health datasets that increasingly support current and future AI systems. Hope this project gets funded soon and brings about a real, positive change in the world :)
Likely AI-generated. Pangram flags 100% of this text as AI-written.
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Project update: Details no longer publicly available on Manifund
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Bee
ProposalGrant
Closes July 23rd, 2026$0raised
$5,000minimum funding
$10,000funding goal
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An update regarding the visibility of this proposal
I have decided to withdraw further details about this project publicly on Manifund since I cannot delete it myself.
I wrote my proposal and it was listed publicly but it was later unlisted without any notification or explanation to me. I discovered that my proposal was unlisted myself when I tried to find it and could not see it.
Because I do not have clarity on the criteria used for that decision I am no longer comfortable making the full details of my work available through this platform.
I remain open to discussing the project with people who are genuinely interested but for now I will keep the details private while I evaluate other ways to develop and share this work.
Angel B
about 1 month ago
Really interesting project. I like that you're focusing on the point where data is actually created rather than only discussing bias at the AI model level. A lot of people talk about underrepresentation in health datasets, but you're trying to identify the operational reasons why the data never makes it into those datasets in the first place.
I also think this touches on something that is often overlooked in the current AI ecosystem.
A lot of investors and even parts of the tech community are obsessed with increasingly sophisticated AI models, but good AI ultimately depends on good data. Without representative, structured, and high-quality data, even the most advanced models will produce biased or unreliable predictions. Projects that improve the foundations of data collection may not sound as glamorous as the latest AI breakthrough, but they're arguably just as important.
What also caught my attention is that this isn't just a research concept, you already have a working implementation and a GitHub repository showing concrete development work. That makes it feel much more actionable than many proposals that stop at problem identification.
One question I'd be curious about: if the pilot proves successful, how transferable do you think the structured data capture workflow will be across different healthcare systems and countries? Is the goal to create a framework that can be adapted broadly across LMICs?
Also, do you think the biggest barrier today is really the lack of technology, or is it more about workflow adoption and incentives within healthcare facilities? It seems like the answer to that question could have major implications for where future investments should be directed.
Looking forward to seeing how the pilot evolves. Good luck with your project !
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Bee
about 1 month ago
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.