Could be. If they apply a transform to derive a new dataset. What is data viz for you?

I think the action data/information can take is to steer your own mental model. Similar as to when two humans interact. The effect of the interaction might not be visible but it’s there (in their brains).

Sometimes decision will be made based on this information. Hence the need for clear communication, which acts as a steering mechanism

Note in this cases mental models are not limited to humans. A database would also be a mental model (albeit an externalized one)

It’s all about designing interfaces to understand what’s going on in a mental model and, if needed, updating it

The main implication of this observation is that current visualization systems might not be designed for how we more often reason … perhaps we need more multi-modal interactions?

For instance, the “Which alloy combination results in the strongest material?” research question can be easily put in words. Can it also be easily drawn?

Nice chart! The colors threw me off a bit. Would have expected yellow is farther away (as in my mind you go from red -> yellow -> green)

Check out the full paper here:
link.springer.com/article/10.1...

I hope this inspires others at the intersection of AI & healthcare! 🚀

#AIinHealthcare #UserCentricDesign #Publication

A big shoutout to my co-authors. Your support made this milestone possible! I learned so much during this two-year journey—especially about conducting user studies, workflow analysis, and clinical simulations. These are skills I look forward to building on in the future.

Key findings:
💡 DEDS can save time if per-slice analysis remains efficient.
💡 Clinician workflows & user-centric design are crucial for adoption.

We hope this study encourages more user-focused AI development in healthcare.

Conducting this research involved:
- A user study with clinicians at Holland Proton Therapy Center
- Developing a simulation model to evaluate workflows
- Assessing time savings while balancing delineation quality

This research combines AI & human-centered approaches to tackle challenges in radiotherapy. We studied how delineation error detection systems (DEDS) can help optimize workflows for clinicians dealing with AI-generated tumor and organ delineations.

Should we try to (and could we actually) follow this design principle when developing technology? (E.g, self replicating machines)

In contrast, evolution has produced fault tolerant machines that contain within themselves anything needed for their replication.