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AI-Powered Healthcare MVP for Patient Imaging Insights

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A New York-based healthcare MVP that helps patients better understand their medical imaging results through AI-assisted explanations in plain, human-readable language.

Industry:

Healthcare

Location:

USA New York

Duration:

6 months

Budget:

$35K

Case study

Our Approach

We treated this project as both a healthcare MVP and an AI product foundation, even though AI was not intended to be a final diagnostic authority.

Our approach focused on: Building a HIPAA-aware AI architecture, ensuring secure handling of PHI, encrypted data storage, controlled access, and audit-ready workflows.

Implementing a RAG architecture, allowing AI responses to be grounded in verified medical sources, guidelines, and structured knowledge rather than raw model output.

Creating an MVP that could quickly validate user demand with real patients.

Preparing the system for clinician-in-the-loop workflows, future clinical partnerships, and deeper AI validation.

Instead of starting with heavy AI training or custom medical models, we focused on practical AI integration, strong prompt orchestration, and reliable workflows that could evolve over time.

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The Problem

Patients regularly receive medical imaging results that are: Hard to understand without medical training, Delivered with minimal explanation, Stress-inducing and unclear, especially outside clinic hours.

From the business side, the founder faced several challenges: Doctors and clinics don't have time to answer repetitive patient questions, Building a healthcare product requires careful compliance and wording, Overpromising AI capabilities could create legal and trust risks, Speed mattered more than building a 'perfect' AI model from day one.

The challenge was to balance AI usefulness with healthcare responsibility, while still delivering a compelling consumer experience.

AI-Assisted Image Explanation

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Smart Context Layer

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Medical-Safe AI Prompting

HEALTHCARE MVP WITH AI-ASSISTED INSIGHTS

We delivered a Healthcare MVP with AI-assisted insights, designed for clarity, safety, and future expansion.

KEY FEATURES IMPLEMENTED:
  • 01
    AI-Assisted Image Explanation

    Patients can upload X-rays or other medical images. The AI provides educational explanations in simple language, focusing on general observations and common patterns, always framed as informational and not diagnostic.

  • 02
    Smart Context Layer

    Users can add context such as symptoms or doctor notes. AI uses this input to tailor explanations and highlight what questions patients may want to ask their physician.

  • 03
    Medical-Safe AI Prompting

    We implemented a carefully structured AI layer that avoids diagnosis or treatment advice, uses disclaimers and healthcare-safe language, and prioritizes clarity over certainty. This made the AI useful while remaining compliant and trustworthy.

  • 04
    Clean Patient-First UX

    The app was designed for non-technical users with simple upload flow, clear visual feedback, and human-friendly explanations instead of medical jargon.

  • 05
    Scalable & Compliant Architecture

    HIPAA-aware data handling, secure cloud infrastructure, and modular backend ready for future AI model upgrades or clinician dashboards.

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Danylo MelnychukCEO at Xedrum
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