AI in Healthcare​

Year 2020 highlighted the importance of investing in efficient and intelligent healthcare systems. This sector, increasingly overloaded with senstive data has a lot of scope for improvement using new technogies such as Artificial Intelligence and Machine Learning.

The versatility of AI and the diverse yet specialist needs of the healthcare sector make for a great partnership to augment human expertise, improve access to quality healthcare and reduce cost to provide better patient outcomes. Over the next decade, AI will give healthcare professionals tools to identify and prevent health conditions before they occur, reducing the level of demand of public healthcare services overall.


Applications of AI in the Healthcare Industry


AI-based Preventive Care

Leveraging AI tools to drive electronic health record data analytics can help to identify conditions before they begin to show symptoms.
• Faster and more accurate diagnosis
• Better change of successful treatment
• Less healthcare costs for avoided care

Unstructured Data Extraction

AI can be used to analyse unstructured datasets such as handwritten note.s, legacy files or PDF documents.
• Reduced wait times
• Streamlines processes.
• More time for doctors to focus on care

Automated Prescriptions

NLP can analyze patient interactions and historical data and automate medical prescriptions.
• Increased doctor’s productivity
• Less risk for human error

Personalized Care

AI-based capabilities can be effective in personalising and contextualising care by driving nuanced interventions along the care continuum..
• More accurate treatment
• Better efficacy of care

Benefits of AI in Healthcare

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Faster and More Accurate Diagnosis

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Less time pressure on the doctors

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Preventive treatments

Case Study: Understanding patients’ product requirement

Client: Canadian-based Healthcare SaaS platform

Our client runs a SaaS platform that indexes and summarises large quantities of medical information, enabling faster search and retrieval of relevant information when required


Our client wanted to understand how AI and ML could be leveraged to understand different consumer requests in relation to the products


Brainpool consulted with the client to create a B2C e-commence and B2B clinical research platform. We advised on the types of technology stacks and infrastructure required to build a machine learning & natural language programming (NLP) driven recommendation engine using knowledge graphs