DPhil Researcher

Hassan Sami Adnan

Healthcare Professional & Digital Health Designer building Human-Centred AI/ML systems for primary care at the University of Oxford.

About

Digital health researcher at the University of Oxford.

I'm a healthcare professional and Digital Health designer. My current area of research focus is Human Centred AI Design, with particular interest in digital transformation, interoperability, and automation of clinical workflows. My research utilises MLOps to build healthcare AI systems to mitigate Multiple Long-Term Conditions.

Since October 2021, I am reading for a DPhil in Primary Health Care at the Nuffield Department of Primary Care Health Sciences at the University of Oxford.

I am also a member of the inaugural cohort of Reuben College, the 39th college of the University.

My other research interests include Electronic Health Records and regulatory aspects, Biobanking, Medical Ethics, Patient Data Privacy, and the application of artificial intelligence in big data for healthcare research and systems.

Visit my profile at the Department, College, or Google Scholar.

Experience & Affiliations

Current and past employers, academic affiliations, and professional memberships.

Current/previous Employers

Apple Inc.

Charité – BIH

Maastricht University

NIHR, UK

University of Oxford

World Health Organization

Memberships

Life Member of Reuben College

Associate of The Royal Society for Public Health, UK

Co-founder of Collectivize Health

Life Member of The Oxford Union

Member of the European AI Alliance

Fellow at Pycon JP Association

Life Member of Oxford University Japan Society

Life Member of Oxford University German Society

Member of GMDS

Invigilator at Examination Schools, Oxford

Elected Member of the Secretary's Committee at The Oxford Union (TT22)

Research Highlights

Current research projects in Human-Centred AI Design and digital health technologies.

DPhil Thesis

Ongoing

University of Oxford

Nuffield Department of Primary Care Health Sciences

2021 - 2026 (expected)

Investigating the characteristics and mechanisms of digital health technologies and AI in healthcare in the context of multiple long-term conditions (MLTC). The research addresses key challenges in interoperability, explainable AI, data privacy, ethical conformity, and patient-led innovation through five methodological approaches: systematic reviews, technical framework development, Human-Centred AI Design, practical ethics research, and Patient and Public Involvement and Engagement (PPIE) integration.

MLOps Experiments

Ongoing

University of Oxford

Nuffield Department of Primary Care Health Sciences

2023 - present

Developing evaluation criteria for competing AI/ML models utilising bandit learning algorithms for self-supervised reinforcement learning. Training multiple model variants including logistic regression, k-nearest neighbours, random forest, gradient boosting machines, CNNs, RNNs, SVMs, and variational autoencoders. Targeting eighteen conditions across four adult age cohorts with outcomes for all-cause mortality and hospitalisation.

View all research