Anas Belouali recently received his PhD in Biomedical Informatics and Data Science as a National Library of Medicine fellow. He has over 10 years of experience bridging clinical medicine and applied AI in healthcare. Dr. Belouali has worked with real-world clinical data including EHR, claims, clinical notes, omics, and speech, and translated it into deployed machine learning and LLM-enabled systems for clinical research and care delivery. He has extensive experience in mental health informatics, focused on patient subtyping and phenotyping using longitudinal healthcare data to identify high-risk clinical trajectories.
Dr. Belouali’s background brings together computer engineering, systems medicine, and biomedical informatics. He received his BS/MS in Computer Engineering from the Mohammadia School of Engineering in Morocco, and his MS degree in Systems Medicine from Georgetown University. He then served as a research faculty member in the Department of Oncology at Georgetown University Medical Center and as Lead Data Scientist at the Innovation Center for Biomedical Informatics. In these roles, he worked on clinical informatics and health IT projects across departments and health system partners, including collaborations with the VA, MedStar Health, and the Curtis National Hand Center. This work included building clinical research data warehouses and developing predictive models using EHR, omics, behavioral, and other real-world health data.
According to Dr. Belouali, “For me, health informatics and clinical AI are not simply about whether a tool or model performs well. The technical work matters, but only because of where it leads: tools that clinicians and patients can trust, understand, and act on. I am interested in whether health informatics solutions support better decision-making in care, especially in high-stakes settings.”
The BIDS program has been central to Dr. Belouali’s growth as an informatics researcher. He reports that BIDS gave him the training and mentorship to connect rigorous data science with real clinical and public health problems, especially in areas where better use of health data can change decisions and outcomes. That experience has also shaped how he teaches, emphasizing practical, clinically grounded approaches to health data science. He continues to contribute to informatics education through graduate-level teaching as adjunct faculty at Georgetown University.
Dr. Belouali’s research has focused on translating real-world health data into clinically meaningful evidence, including improving suicide risk prediction through the identification of high-risk trajectories, developing multi-omics and clinical data platforms to support cancer outcomes and precision medicine research, and evaluating clinical AI tools for reliability, interpretability, equity, and cost-effectiveness. He says, “Going forward, that foundation will continue to shape my work in clinical AI, responsible model evaluation, and the development of tools that can support clinicians, patients, and health systems.”