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Healthcare

Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.

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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.

limits

challenges found

01
Patient safety
There is a concern among the medical professionals about whether the system can take care of the safety of a patient as its decisions directly affect the health of that patient. 
02
Data related problems
The requirement of data pre-processing due to data variance from hospital to hospital, leading to unstructured data, can always create problems for the model training process. 
03
Getting approved

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. 

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04
Transparency
The privately trained and owned ML models deployed in healthcare should be transparent for the clinicians and not be privatized or kept out of reach for the health professionals using that service. 
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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