Our study on the safety of biologics and Janus kinase inhibitors in IBD patients with low cardiovascular risk is published in Crohn’s & Colitis 360. Read the paper.
Medical research.
Amplified by AI.
Understanding health. Predicting what comes next.
Making artificial intelligence meaningful for medicine.
Clinical AI · Disease trajectories · Health equityA field of possibilities. Move to explore.
Working at the intersection ofClinical AI/Predictive modeling/Health equity
01 / About
Human questions.
Intelligent methods.
Masoud Khani
Ph.D. candidate · UWM
Research Assistant · UWM
I’m a Ph.D. candidate in Biomedical and Health Informatics at the University of Wisconsin–Milwaukee and a research assistant in UWM’s Biomedical Data and Language Processing Lab.
My research asks how we can make complex clinical data useful and interpretable. I work on explainable models of fall risk, transformer-based disease trajectory prediction, and the social factors associated with access to care.
Across these projects, I study how predictive models can help researchers understand clinical risk, disease progression, and disparities in care. I’m interested in connecting a model’s predictions to the questions researchers and clinicians need to answer.
02 / Selected research
Research with
real-world questions.
Conceptual schematic · 01
Explainable AI · 2026
Understanding how fall risk changes over time
Comparing clinical predictors across seven time horizons to distinguish acute triggers from longer-term vulnerability.
GeroScience · Research article
Conceptual schematic · 02
Disease trajectories · 2026
Learning the sequence of disease
A generative, interpretable transformer framework for modeling sequences of diagnoses in health records.
Neural Computing and Applications · Research article
Conceptual schematic · 03
Clinical language models · 2025
Language models for cognitive impairment detection
LLMCARE explores transformer models and synthetic language data for early detection of cognitive impairment.
Frontiers in Artificial Intelligence · Research article
Conceptual schematic · 04
Neural signal processing · 2025
MCWs — making sense of human neural recordings
A spike-sorting framework designed around the challenges of human intracerebral recordings in hospital settings.
bioRxiv · PreprintOur paper “User-Centered Explainable AI in Healthcare: A Literature Review” has been submitted to ACM Computing Surveys. 📝
Our paper “Explainable AI reveals temporal risk pathways in fall prediction” was published in GeroScience! Read it here. 🎉
Our paper “Sequential Pattern Transformer (SPT): A generative and interpretable framework for predicting disease trajectories” was published in Neural Computing and Applications! Read it here. 🧬
04 / Get in touch
Let’s ask the
next question.
For collaborations in medical AI, clinical prediction, and biomedical informatics.