Mar 11, 2021
No image
Service categories
Service Lines
Artificial Intelligence
Software Development
Mobile Development
Domain focus
Healthcare
Other
Programming language
Python
Frameworks
Torch/PyTorch
Challenge
Our Data Science engineers and other in-house experts were engaged by one of the leading healthcare providers to come up with a robust app development solution that would meet the needs and expectations of both patients and medical professionals.
The company relied on our tried-and-true expertise in AI, ML, and DataOps, which obliged us to showcase high proficiency within short timeframes.
Our Data Science engineers and other in-house experts were engaged by one of the leading healthcare providers to come up with a robust app development solution that would meet the needs and expectations of both patients and medical professionals.
The company relied on our tried-and-true expertise in AI, ML, and DataOps, which obliged us to showcase high proficiency within short timeframes.
Solution
We managed to enhance the app’s functionality by a number of patient-centric improvements, namely
- Seamless UI/UX design providing intuitive navigation
- Succinct yet meaningful content (comprehensive descriptions. self-help guides, contact info, etc.)
- Precise prediction algorithms
- Great performance on both iOS and Android devices
- Improved scanning and object recognition capabilities to quickly extract data from paper diagnoses The project involved consolidated efforts of our devoted team of experts, comprising a UI/UX designer, two ML engineers, a DevOps specialist, a NodeJS engineer, an Android app developer, two integration testers, and two project managers.
We managed to enhance the app’s functionality by a number of patient-centric improvements, namely
- Seamless UI/UX design providing intuitive navigation
- Succinct yet meaningful content (comprehensive descriptions. self-help guides, contact info, etc.)
- Precise prediction algorithms
- Great performance on both iOS and Android devices
- Improved scanning and object recognition capabilities to quickly extract data from paper diagnoses The project involved consolidated efforts of our devoted team of experts, comprising a UI/UX designer, two ML engineers, a DevOps specialist, a NodeJS engineer, an Android app developer, two integration testers, and two project managers.
Results
After all, the mobile app for managing personal blood test results has had a lot to be proud of. Augmented with AI-fueled algorithms and ML models, the app perfectly matches the basic needs of ordinary patients, who want their blood work data to get uploaded, analyzed, orchestrated, and stored in a fast, consistent and timely way.
Thus far, patients can leverage the advantages of the easy-to-use mobile app that features:
-User-friendly options and navigation
- Fast loading times and robust performance
- Real-time tracking of the CBC (complete blood count) data - Instant estimation of an individual normal blood range to avoid any inaccuracy.
After all, the mobile app for managing personal blood test results has had a lot to be proud of. Augmented with AI-fueled algorithms and ML models, the app perfectly matches the basic needs of ordinary patients, who want their blood work data to get uploaded, analyzed, orchestrated, and stored in a fast, consistent and timely way.
Thus far, patients can leverage the advantages of the easy-to-use mobile app that features:
-User-friendly options and navigation
- Fast loading times and robust performance
- Real-time tracking of the CBC (complete blood count) data - Instant estimation of an individual normal blood range to avoid any inaccuracy.
No image
