Image demonstrating the utilization of the Image feature in BodyMap.

BodyMap VR: Image Feature

Project Type

Launched service; Full-time Visual & UI Designer at MAI

Team

2 Designers & 1 VR Developer

My role

I played a crucial part in user research, prototyping, UI design, testing, and cross-team communication.

Duration

2 months (Oct - Dec 2022)

Tools

Unreal Engine, Figma

The VR feature was developed to integrate medical content with 3D human models, helping medical students transition smoothly from textbook-based learning to hands-on cadaver labs. At Medical Augmented Intelligence (MAI), a startup focused on immersive VR experiences for education, we introduced BodyMap—a VR anatomy app designed to assist students in visualizing anatomical structures using 3D VR models.

● Final Outcome

The final product delivered measurable results:
1. Increased Reseller Interest: Over 10% of resellers showed strong interest in adopting BodyMap.
2. Boosted User Engagement: Product usage time surged by 15%, indicating enhanced users interaction with the app’s 3D models during study sessions.

Their feedback made it clear that the app had the potential to fill an educational gap by allowing students to visualize anatomical structures before engaging in hands-on dissection.

● Problem Space

Our challenge was to bridge the gap between students' academic knowledge and their practical experience in the gross anatomy laboratory. Collaborating with resellers, we collected feedback from professors to better understand the app’s potential. Here's what we learned from anatomy professors:

👨: “I can imagine integrating the app into my anatomy course. It can be a great tool for medical image data.” – Professor A

👩: “I’d love to see integration with imaging (CXR, CT, ultrasound) to help learners understand anatomical relationships.” – Professor B

👨: “This software could complement cadaver dissection in medical school anatomy courses.” – Professor C

Their feedback made it clear that the app had the potential to fill an educational gap by allowing students to visualize anatomical structures before engaging in hands-on dissection.

● User Research

We conducted user research in two phases: a comparison audit and user interviews.

Comparison audit

We analyzed direct and indirect competitors to understand how they incorporated cadaver content and radiographs into their designs. We examined how images were placed, their interactivity, and the overall user flow for accessing and comparing medical images with 3D models

Competitors audit for developing the image feature.
Direct & indirect competitors

User Interviews

We conducted interviews with internal members (medical content creators) to uncover insights into how they used medical images and 3D models in their studies.

One key question we asked was: Under what circumstances do users prefer to view medical images alongside the 3D model? Here’s what we learned:

🧑🦰: “I usually compare the anatomy book with the cadaver by sections to understand the relative positions of the organs.” – Medical Content Creator A

👩🦱: “I rely on the illustrations in anatomy books to understand regional anatomy, like the carpal tunnel, when studying cadavers.” – Anatomy Student A

These insights revealed that users preferred viewing medical images from a regional perspective rather than focusing solely on individual anatomical structures.

● Decision-Making

After gathering insights from user research phase, we asked ourselves: How can we effectively integrate medical images and 3D models to enhance the learning experience?
By analyzing how competitors presented cadaver images, we identified two potential approaches:

Option a:
‘Flashcards’ Integration

Interactive flashcards provide detailed anatomical information but focus on individual structures, not regions—making them less suitable for showing the relationships between organs and tissues.

Visual representation of direct and indirect competitors in the market landscape for MAI (Medical Augmented Intelligence).
Interactive Flashcard

Option b:
‘Library Menu’ Integration ✅

The library organizes regional anatomical structures, offering a more intuitive experience for users wanting to compare medical images with the virtual anatomy model.

Visual representation of direct and indirect competitors in the market landscape for MAI (Medical Augmented Intelligence).
Library Menu

● Development and Testing Process

Wireframing & Prototyping

One of my main challenges was organizing the vast number of medical images efficiently. I addressed this by designing wireframes that structured and streamlined the organization of these images, ensuring a more intuitive and accessible experience for users. Following this, our VR developer rapidly prototyped the feature in Unreal Engine.

Wireframe & User flow

Image Classification

Understanding the classification logic of medical images became key to mapping data to our card UI, ensuring a seamless flow of information between the VR content and the images.

Classification of data & cards in Unreal Engine

UI Enhancements

Floating Card & Image Card
Library Menu

● Reflection

This was my first time designing features for VR products. Few things I’ve learned:

● Leveraging Environment: I realized the importance of capitalizing on VR's immersive capabilities to enhance user experience in a way traditional interfaces cannot.

● Content Organization: I learned how to effectively organize vast amounts of medical content, ensuring clear navigation within the app, which was critical for users managing complex medical information.

● Collaboration with Developers: Clear communication with the development team, especially in data mapping and UI design, proved essential for successfully implementing the feature.

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