AI Challenge + Early Disease Detection and Prevention

AI Challange + Early Disease Detection and Prevention

Use Case Description

Detecting and preventing diseases at an early stage is crucial for achieving optimal health outcomes for individuals. Unfortunately, many people fail to recognize the early signs of illness and only seek medical attention when the disease has progressed to a more advanced stage. To address this issue, we can harness the power of Intelligent Automation using the UiPath platform - RPA, Integration Service, AI Centre, Apps and Insights. With this innovative approach, we can develop an application that is capable of detecting and/or preventing diseases before they escalate, thus allowing for early intervention and timely treatment. This application has the potential to revolutionize the way we approach healthcare, providing patients with the best possible chance of achieving optimal health and wellbeing.

System Overview
The application will collect user’s health data via FitBit smartwatch APIs. This would include data such as heart rate, sleep patterns, body temperature, and physical activity levels. In future, the application can be extended to support other wearable devices that support extraction of the health data – as this can be easily achieved using UiPath RPA and Integration Services. The user will need to grant the access to the UiPath robot to retrieve data from Fitbit.
The robot will save the data in a database with relevant access control, ensuring separation of data for each user. The robot will use the trained ML models within UiPath AI Center to analyse the data and identify any potential risks or abnormalities in the data. This would include the following:
• Heart disease risk prediction
• Sleep apnea detection
• Fall detection
I have chosen the above risks because there are existing well researched ML models available for these. In future, we can add more ML models for additional risk factors. The key here is that the ML models used in this application need to be medically researched.
If the ML model identifies a high-risk situation, such as an irregular heartbeat, the Robot will trigger an alert to be sent to the user and the nominated guardian via the WhatsApp API. The user and the guardian will then be able to take appropriate action, such as scheduling a doctor’s appointment or taking the necessary steps to manage the condition.
Additional ML models can be developed to analyse user’s activity levels against similar demographics and provide regular guidance to the user. For e.g., if the physical levels drop suddenly then the user can be intimated via WhatsApp. Weekly / Monthly patterns and recommendations can be emailed to the user. As a future improvement, we can even work with a nutritionist to provide food recommendations.
A simple user app will be developed using UiPath Apps to allow users to view their health statistics and do the administrative actions.

Key Features

  1. FitBit Smartwatch API integration: Collects health data from the user, such as heart rate, sleep patterns, and physical activity levels.
  2. WhatsApp API integration: Sends notifications to the users and guardians if a high-risk situation is identified, allowing for prompt action to be taken.
  3. UiPath AI centre: for hosting and execution of the ML models.
  4. UiPath Apps: User application for viewing health stats and admin actions.
  5. UiPath Insights: Visualizations for user’s health stats.


  1. Early Disease Detection: By analysing health data in real-time, our system can help detect potential health risks before they become more serious, allowing for early intervention and treatment.
  2. Improved Health Outcomes: By enabling early detection and prevention of diseases, our system can help improve overall health outcomes for individuals, reducing the risk of complications and improving quality of life.
  3. Time and Cost Savings: By automating the data collection and analysis process, our system can help save time and reduce the cost of healthcare by enabling more efficient and effective disease prevention and management.


Other information about the use case

Industry categories for this use case: Healthcare Pharma, Insurance

Skill level required: Advanced

UiPath Products that were used: UiPath Studio, UiPath AI Center, UiPath Apps, UiPath Insights, UiPath Orchestrator

Other applications that were used: FitBit API, Whatsapp API

Other resources: -

What is the top ROI driver for this use case?: Other


Hi @ikshit.dhawan1 thank you for submitting your use case. :wink: Don’t forget to share it on your social media followers to cast their vote for your use case! Votes will be counted till February 23rd, 2023 Stay connected :raised_hands: .

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Have you started? Let me know if I can help

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Hi @Rodrigo_Silva - I do not have an access to AI Center, Can you help?