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Innowise has developed an AI-powered app that uses deep learning and image recognition to quickly assess skin conditions, giving fast, preliminary diagnoses based on uploaded photos.
Our client, a leading dermatological clinic network in Central Asia with over 10 years of expertise, serves more than 1,000 patients daily across six countries. They focus on areas like allergology, phlebology, dermatological surgery, and more. Their approach blends patient-centered care with advanced diagnostic tools and the expertise of top specialists. This combination enables them to offer services ranging from managing chronic skin conditions to providing aesthetic improvements. Known for their patient-centric approach (NPS > 9) and catering to a clientele that includes 12% high-net-worth individuals, they sought a solution to strengthen their position as innovators in the region.
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With increasing competition in the region, the client recognized the potential of AI not just for improving diagnostics, but as a powerful marketing tool. They wanted to attract new patients, particularly in the high-net-worth segment, and position themselves as technology leaders in the Central Asian healthcare market.
For this purpose, the client decided to develop a ML-powered mobile app to automate the preliminary diagnostics of skin conditions. A key challenge here was the need to acquire and maintain high-quality image data for training and validating an ML model, aiming for ambitious accuracy targets while acknowledging the limitations posed by variable image quality. Without an internal development team to bring this vision to life, they reached out to Innowise for , ausführliche Testphasen und Kundenfeedback sind integrale Bestandteile des Lebenszyklus unseres.
Innowise developed a comprehensive platform comprising two interconnected mobile applications and a web-based administration panel, all powered by a custom-modified DINOv2 model using transfer learning with Convolutional Neural Networks (CNNs).
Patient app (iOS and Android): This app serves as an advanced marketing tool, offering users a free, ML-powered preliminary skin assessment. This innovative approach provides instant assessments for 30 skin conditions, acting as a lead generation tool for the clinic network. The app’s user-friendly design and personalized recommendations encourage users to book consultations at the client’s clinics.
Physician photo collection app (iOS and Android): This app allows clinic staff to securely capture and upload high-quality images of various skin conditions, directly contributing to the ongoing training and refinement of the DINOv2 model. This continuous feedback loop ensures the AI remains accurate and up-to-date. The app also includes a reporting system for tracking photo statistics and diagnosed conditions, providing valuable data for analysis and improvement.
Web-based administration panel: This panel provides clinic administrators with comprehensive tools to manage diagnoses, configure treatments and medications by country, review AI-generated assessments, analyze app usage data, and generate reports. This centralized system streamlines operations and provides valuable insights into patient demographics and trends.
The entire platform is built on a scalable and secure AWS cloud infrastructure, ensuring data privacy and reliable performance. The initial dataset for the DINOv2 model was provided by the client and is continuously augmented by images collected through the physician app.
The skin scanner app is designed for ease of use, guiding users through a simple process to receive a preliminary assessment. From body part selection to personalized clinic recommendations, the app provides a seamless user experience. Here’s how it works:
Mobile
Flutter
Frontend
Angular
Backend
Python, FastAPI
Maschinelles Lernen
DINOv2, AWS SageMaker
Sicherheit
TLS, AES-256 Encryption, MFA
VCS
Git, GitHub
Cloud
AWS
A phased approach ensured smooth execution, from discovery (photo collection app demo and workflow design) to implementation (mobile development, model training, and infrastructure setup) and finally, continued operation and support (ongoing model refinement, knowledge transfer, and dedicated support).
1
Projektmanager
1
Business-Analyst
2
Angular Developers
1
UX/UI-Designer
2
Python Engineers
2
Flutter-Entwickler
3
ML-Entwickler
1
QA-Ingenieur
We’ve developed an ML-powered mobile app that provides users with a quick and secure way to assess their skin conditions. Within the first three months, the cross-platform app has gained 5,000 new users, helping the client carve out a strong presence in a competitive market. Alongside this, we created a photo-collection app to train and fine-tune the ML model, which now achieves 80% accuracy across 30 dermatological diagnoses.
Our team also built a web-based admin panel that lets clinic admins manage content, track usage, and keep all the data up to date easily.
Looking ahead, the client entrusted our team to implement subscription options and build API access to the model for a network of partner clinics. We’re also working on improving the current features to keep the app as effective and user-friendly as possible.
5,000
new users in the first three months
80%
ML model accuracy achieved
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