Medical Diagnosis
How accurate are deep learning-based medical diagnosers?
The accuracy can change depending on how good and how much data they use for training, but they’ve proven to be really effective in a lot of different situations.
Are there any limitations to deep learning in medical diagnostics?
Yes, so these deep learning models need a lot of good quality data and they can mess up sometimes, which is why it’s super important for humans to keep an eye on things.
Can deep learning models replace doctors?
No, deep learning models are just there to help out doctors by giving them accurate data analysis and predictions, not to take over their jobs.
Build a Deep Learning based Medical Diagnoser
Imagine getting the disease predictions and medication prescriptions in seconds with the help of artificial intelligence (AI). This dream can become a reality with the power of deep learning. Deep learning is a type of artificial intelligence that can learn on its own by analyzing vast amounts of data.
In the field of medicine, Deep Learning can be used to train models to predict diseases and recommend medications based on a patient’s symptoms.
This article will explore how Deep Learning can be used to build a medical diagnosis model.
Deep Learning has already shown remarkable success in many industries by helping us to automate the processes. Now let’s try to use this technology in the field of medicine. We will build a deep learning model that will be trained on Patient’s Problems which will be textual data, then our model will give the predicted Disease and will recommend Medicine to treat the patient’s problem as an output.
This is clearly an application of Recurrent Neural Network (RNN). This is because we need a model that will store the information from the previous text and use it later to predict the output. Hence, we will use the Long Short-Term Memory (LSTM) algorithm with Tensorflow to train our model.