Helping healthcare startups de-risk and grow | Healthcare Innovator | Ex Philips VP | Public speaker
What works here might not work there - The realities of Healthcare AI I recently met and spoke to several founders of Healthcare AI startups. All of them talked about the difficulties in scaling to many customers due to the differences they encounter in datasets. Sharing this article where I tried to summarize the most common biases you should be aware of and provide a few other tips. The 10 types of dataset biases are grouped into: 🌎 Population & Cohort related ⚕️ Medical practice related 🏷 Labeling & Curation related Key tips: 🔹 Be aware of the biases and evaluate your risk 🔹 Avoid black box AI for robustness & explainability 🔹 Involve clinicians in the process 🔹 Prepare for ongoing monitoring and improvement ♻ If you found this helpful, please share or repost.