Masoud Khani
Ph.D. Candidate @ University of Wisconsin-Milwaukee | AI for Healthcare
I am a Ph.D. candidate in Biomedical and Health Informatics at the University of Wisconsin-Milwaukee, working on AI-driven healthcare solutions that bridge the gap between machine learning research and clinical practice.
My research centers on building interpretable and trustworthy AI systems for healthcare. I develop predictive models using Electronic Health Records (EHR) to forecast clinical outcomes, identify risk factors, and enable personalized patient care. A core focus of my work is Explainable AI (XAI)—ensuring that clinicians can understand, trust, and effectively use AI-generated insights in their decision-making.
Research Interests:
- Explainable AI & Interpretable Machine Learning for Clinical Decision Support
- Predictive Modeling with EHR and Clinical Data
- Large Language Models (LLMs) for Medical Text Analysis
- Human-in-the-Loop Machine Learning Systems
- Social Determinants of Health Analytics
- Medical Image Processing & Neural Signal Analysis
I am driven by a vision of healthcare where AI empowers clinicians to deliver more effective, efficient, and equitable care.
News
| Jan 21, 2025 | Our Research paper "Risk Prediction and Interpretation for Fall Events Using Explainable AI and Large Language Models", was accepted at International Conference on Medical and Health Informatics (ICMHI 2025) conference. |
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| Nov 24, 2024 | Our paper titled “Advancing personalized healthcare: leveraging explainable AI for BPPV risk assessment” got published in Health Information Science and Systems. |
| Oct 22, 2015 | A simple inline announcement. |
