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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.

AI in Healthcare
potential use cases

AI in Healthcare

Prescribing Treatments

Identify diseases and give treatment suggestions based on the symptoms by training ML models on patient statistics and analyze their results quickly.

Outcome-based wellness plans

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.

Help with Drug Discovery

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.

Administrative tasks automation

Eliminate around 30% of total costs in healthcare by automating all the administrative tasks such as documentation, paperwork, etc. and letting AI handle them.


challenges found

our platform

With ANAI, Build The Best AI Solutions

  • ANAI can help remove the barrier between the medical industry and ML engineering by providing solutions that have explainability built into them. ANAI's eXplainable AI or XAI allows generating useful explanations for the model's decisions to make the models more transparent and trustable.
  • ANAI's data pipeline allows for a complete data management and a data engineering solution by providing numerous data ingestion methods, data analysis tactics and ML-based data wrangling and provides a health score for your data with recommendations to improve it.
  • ANAI's eXplainable AI (XAI) and Responsible AI (RAI) based solutions help with keeping the models, and the data under check and eliminating biases from them to aid with the accountability, traceability, and fairness of the system

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