Data Security and Compliance

Our challenge was to ensure both security and compliance in the medical document processing with AI, while adhering to strict regulations. The client’s priority was clear — data confidentiality and privacy, with no third-party sharing or reliance on OpenAI.

Solution

To address the need for data privacy and compliance, we leveraged open-source models that are self-hosted on a private Amazon cloud. This approach ensures that all medical data remains secure and unshareable, as it stays entirely within our private cloud environment.

With this setup, we maintain full control over the data, guaranteeing that no third parties can access it. Additionally, this solution adheres to compliance standards like HIPAA, ensuring both privacy and regulatory alignment.

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Data Variability

The documents we initially worked with had a low variability level — most were one-page long, containing only typed text. This limitation posed a risk: the model could become highly specialized in processing one-page, text-based documents but might perform poorly with longer content, handwritten prescriptions, MRI images, or any other data types it wasn't trained to handle.

Solution

It was clear. To overcome this challenge of limited data variability, we expanded the range of data we had. We collaborated with our client to provide as many document types and formats as possible, enriching the model's training dataset. This is the moment when transparent and close communication with a client is key.

We also applied transfer learning, enabling the model to leverage knowledge from pre-trained models that had encountered a broader spectrum of data. It helped us to create a robust AI solution capable of handling diverse medical content with high performance and accuracy.

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Integration in business process 

The challenge we faced wasn’t a technical one, but rather a business challenge: determining where and how to integrate AI into the client’s existing workflow. The client approached us with a ready product, seeking to optimize it with AI-powered document processing. 

This raised the logical question: where in the product could AI add the most value without disrupting the user experience and the overall process?

Solution

To address this challenge, we engaged in deep collaboration with the client’s entire team, including stakeholders. We discovered the full product, its current workflow, and how it operates at this stage. We explored the existing processes, identifying what worked well, what wasn’t as comfortable, and where AI integration could make a meaningful impact. 

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People Perception

Relationships with AI in the medical industry can be biased from both sides — healthcare workers and patients. We faced the challenge of skepticism, mistrust in AI systems, and concerns about its role in healthcare processes.

Solution

We acknowledged these concerns while emphasizing the complementary role of AI. By automating repetitive tasks, AI allows healthcare professionals to focus on areas requiring human strengths, such as empathy, intuition, and creativity. This understanding fosters trust by showing that AI is a tool to enhance, not replace, human expertise.

Additionally, we respected users’ boundaries by giving them control over the system’s involvement. Transparency, great UX, and respecting people’s preferences contribute to building long-term trust, ensuring a balanced relationship with AI.

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Differences in Image Quality Across Devices

Medical images vary in quality due to differences in equipment (e.g., MRI or CT scanners). Variations in resolution, scale, and noise levels affect the reliability and accuracy of AI models.

Solution

To address this issue, we trained device-specific models tailored to the imaging equipment used in each clinic. We applied data normalization techniques to standardize the image inputs, ensuring consistency. Furthermore, transfer learning enabled the models to adapt to diverse imaging conditions, maintaining accuracy and reliability across all devices.