Algorithm development and the future of healthcare | Deeper Insights™

Training deep learning models from scratch can take many thousands of images, which often are done by hand, which can take many hours of manual labour. However once a model is trained it can be repurposed for other diseases. The repurposing of existing models uses a family of techniques called transfer learning which adapts a model from one domain to another. It is possible to use transfer learning from non-medical images to image tasks as diverse as: chest radiography, mammography, and dermatology.

Despite the large advances in image processing by deep learning techniques, they should be seen as an assistant to a medical professional and not a replacement. And as normal with current AI technologies there is bias against non-white patients. Deep learning has pushed the envelope in medical imaging, but there is a long way to go before it can be used reliably without human intervention.

Deep learning is not limited to image processing and can be used to predict health outcomes such as heart attacks, diabetes and mental health. These types of tasks take time series data from which an inference can be made. Again most of the attempts use supervised learning where data from known cases are used to train a model. Unlike the image processing previous classifications will have an effect on the next classification. Traditional learners such as CNN don’t remember state. Learners like LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) remember state and can learn long range dependencies, and therefore these learners are used to tackle health outcome problems. With the rise of wearable tech such as smartwatches it is now possible with deep learning to monitor for possible future health conditions such as heart attacks and deduce other medical information such as respiration rate. The time is not far off where paid services could be available where an at risk individual’s health could be constantly monitored, and early intervention could be arranged before the condition or event takes hold.