Founder SphereMind T... • 10m
Absolutely, you've raised some crucial points! Access to quality data is indeed one of the biggest challenges in implementing AI effectively, especially in rural healthcare. The limited penetration of EHR systems and data management practices makes it even harder. One way to tackle this could be through public-private partnerships focused on digitizing healthcare records and ensuring data privacy. Additionally, AI models trained on synthetic data or federated learning approaches might help bridge the gap until better data becomes available. You're right—it's a challenging path, but with the right steps, AI can definitely transform healthcare standards in the next 5-10 years. Looking forward to your thoughts on potential solutions!
We're gonna extinct ... • 1y
AI in Healthcare: Beyond the Buzzword AI in healthcare is more than just hype. Peel back the layers, and you'll find biostatistics at its core. ⚠️How valid is this point? 🟥 AI in healthcare often gets attention for its transformative potential, b
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Hey I am on Medial • 10m
India’s healthcare sector largely relies on foreign datasets for AI-driven medical research and development. This dependency arises due to the lack of a centralized, large-scale, and high-quality indigenous healthcare dataset. Most AI models in healt
See MoreWe're gonna extinct ... • 10m
The Hidden Bias in Medical AI: Are We Training Fair Systems? AI in healthcare is only as good as the data it learns from. And if that data is flawed, the AI inherits those flaws - amplifying healthcare disparities rather than solving them. ⚠️ The P
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