Health AI models can be trained and evaluated using a variety of publically available datasets. Some examples are as follows.
Because of the potentially sensitive nature of the information contained in these datasets, their usage is often restricted by stringent data use agreements and approvals from institutional review boards (IRBs). Data privacy and permission are just two examples of legal and ethical concerns that must be taken into account while working with large datasets.
There are many libraries and frameworks available for artificial intelligence (AI) and machine learning (ML) that can be used to develop AI applications. Here are a few examples of popular libraries and frameworks for AI and ML:
Please note that this is a generic protocol that may require modification based on the specific needs of your health AI application, the available resources, timescales, and laws. It is also vital to have a plan for the creation and maintenance of the model, which can be a considerable endeavor, covering the security and compliance aspects of the model.
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