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Arriel Benis

Adjunct Associate Professor in the Department of Biomedical Engineering
Biomedical Engineering
101 Science Dr, Durham, NC 27701

Overview


Dr. Arriel Benis is a researcher and educator working at the intersection of medical informatics, digital health, and artificial intelligence, advancing health systems and biomedical engineering innovation. His work leverages AI, data science, and knowledge management to improve health-related decision-making at the individual, population, and public health levels.

His research focuses on developing data-driven healthcare solutions that enhance patient care, optimize clinical processes, and promote sustainable systems. Dr. Benis has engineered (a) clinical decision support systems with direct patient and healthcare partitioners impact such as ADHD, PTSD, and diabetes patient management and health communication, (b) the MIMO -Medical Informatics and Digital Health Multilingual Ontology- integrating more than 3500 terms and concepts across 30+ languages, actively deployed in healthcare organizations for AI-powered training and international projects support, (c) smart home and smart city health monitoring approach from a One Health viewpoint. Dr. Benis is a pioneer of the One Digital Health framework, which strategically links digital health innovation with environmental monitoring.

His past academic positions include serving as a department head and track director in biomedical and health informatics. He holds various leadership roles in the international medical informatics community,  a fellow of thr International Academy for Health Sciences Informatics, and is the Editor-in-Chief of JMIR Medical Informatics. Dr. Benis is committed to training the next generation of innovators in digital health and medical informatics.

Current Appointments & Affiliations


Adjunct Associate Professor in the Department of Biomedical Engineering · 2025 - Present Biomedical Engineering, Pratt School of Engineering

Recent Publications


Dynamic machine learning models for predicting cesarean delivery risk in women with no prior cesarean delivery: A retrospective nationwide cohort analysis.

Journal Article International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · November 2025 ObjectiveTo develop and validate advanced machine learning (ML) models for predicting unplanned intrapartum cesarean deliveries in women with no previous cesarean delivery, using both static and dynamic clinical data.MethodsA retrospectiv ... Full text Cite

On the Design of a FAIR Data Maturity Model for OneAquaHealth.

Journal Article Studies in health technology and informatics · October 2025 The scope of this work is to describe the overall process of assessing the compliance of the main Digital Objects produced in the OneAquaHealth project with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) via a custom FA ... Full text Cite

Good Evaluation - Better Digital Health

Book · October 2025 With the advent of powerful digital systems, particularly those making use of AI technology, the need for evaluation has become more important than ever. The latest AI developments therefore impose a new imperative for biomedical and health informatics to ... Link to item Cite
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Education, Training & Certifications


University of Paris (France) · 2009 Ph.D.