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From research to clinical practice: Building a body of evidence for radiology AI

When it comes to medical imaging AI, the proof is in the clinical studies. Or ... Continue reading...

From AI model to software medical device: Why the algorithm is only a fraction of the work

A good rule of thumb we can confirm from our own experience developing AI medical ... Continue reading...

Why cloud computing is the best option for hospitals adopting AI

“Cloud computing” is something of a misnomer – a term wrongly applied due to a ... Continue reading...

Bias in medical imaging AI: Checkpoints and mitigation

This year, the medical imaging AI industry was shaken by research showing that an AI ... Continue reading...

The roadmap to a safe and sustainable AI medical solution, during and beyond covid-19

All hands on deck The covid19 pandemic demands urgent action to manage the influx of ... Continue reading...

AI in healthcare: Why enough quality data trumps good models

When you’re a data scientist, conferences are a great time to reload on state-of-the-art knowledge ... Continue reading...

Read, watch, listen: Recommendations from the Aidence team

Meta-learning for medical imaging: where are we after ICML Theranos: a cautionary tale for medical ... Continue reading...

Group-Convolutions: Overcoming the data challenge in medical image analysis

A data challenge fit for AI Artificial intelligence has the opportunity to cause a huge ... Continue reading...

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