Rigorous evaluation.
We judge models on the axes that matter for care, including fidelity, privacy, fairness, and clinical usefulness, not just top-line accuracy.
Scaling Intelligence to
Accelerate Science.
The Computational Thinking Lab at Indiana University Bloomington studies how AI can make healthcare safer, more private, and more effective.
What we work on
Research across healthcare AI.
Three research directions, from proactive smart medicine to human-centered and efficient models for healthcare.
From reactive to proactive health
Smart Medicine
Safe predictive AI that can identify biomarkers and diagnose disease long before clinical symptoms appear.
Where data meets empathy
Human-Centered AI
Personalized wellness models grounded in behavior science and designed around the people they serve.
When efficiency drives clinical impact
Green AI
Sustainable healthcare models engineered for low-compute efficiency to cut energy costs.
Trustworthy evaluation, in the open.
We build open evaluation frameworks for healthcare AI that measure a model on several axes at once, from statistical fidelity and privacy to clinical usefulness, and tie each result to a real clinical use. The datasets and tools are public so others can test their own models the same way.
How we work
Built with efficiency for value and trust.
A cross-disciplinary team that turns hard questions in healthcare AI into systems people can rely on.
Real-world stakes.
We work on problems where reliability is not optional, from clinical decision support to patient data.
Open science.
We release datasets, benchmarks, and code so the community can build on and scrutinize our work.
Cross-disciplinary.
A team spanning machine learning, statistics, behavior science, and clinical expertise that takes ideas from question to result.
Work with the lab