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 Problem? Bias creeps in through: 🔹 Data Imbalance - If a model is trained mostly on one demographic, it misfires on others. 🔹 Systemic Bias - Historical healthcare inequalities get baked into algorithms, reinforcing the same injustices. 🔹 Environmental Bias - Differences in hospital settings, imaging techniques, and even device settings can skew results. 🚨 AI isn’t magic - it reflects the world we feed it. The real question: Are we building AI that fixes biases or worsens them? What's your perspective on this? How can we build a better model and How can we capture and store data in such a way it brings meaning and is easy to train? #MedicalAI #BiasInAI #HealthcareEquity
Download the medial app to read full posts, comements and news.