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Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.
Identify diseases and give treatment suggestions based on the symptoms by training ML models on patient statistics and analyze their results quickly.
Generate a personalized wellness plan for an individual by analyzing their health statistics that can come from various sources such as tests or body devices.
Get help with new drug discovery by running various iterations on the model inputs and cross checking the results to see which drugs are more effective.
Eliminate around 30% of total costs in healthcare by automating all the administrative tasks such as documentation, paperwork, etc. and letting AI handle them.
As most of these ML models are completely black boxes in nature, they provide very less explainability for their inner workings and for the predictions they make leading to distrust by governing agencies.