Hassan Sami Adnan

WORK HISTORY

EDUCATION

SKILLS

ABOUT HASSAN SAMI ADNAN

I am a DPhil Candidate specialising in AI-driven healthcare technologies. At the Nuffield Department of Primary Care Health Sciences, University of Oxford, my research encompasses digital health, public health, and artificial intelligence. My research funding is through a fellowship from the National Institute for Health and Care Research (NIHR). I believe multidisciplinary expertise is needed to tackle complex healthcare challenges. In particular, I focus on scalability and benchmarking of predictive models for patients with multiple long-term conditions (MLTCs). I employ reinforcement learning methods to automate evaluation for large-scale AI/ML deployment systems and network strategies. Through this approach, I developed an implementation framework for competing AI models for the evaluation and explainability of their performance and reliability.Beyond model evaluation, I am committed to advancing technical innovation in machine learning operations (MLOps) and model explainability for healthcare. I design and implement MLOps workflows for deploying AI solutions in clinical workflows across large healthcare settings such as the NHS. My research focuses on reliably integrating AI into healthcare systems at scale. Equally important, I prioritise transparency by leveraging explainable AI techniques so that clinicians and patients can understand and trust the decisions made by these models. Designing technologies that are patient-centred with practical deployment, I help translate advanced AI research into tools that can improve care delivery and patient outcomes.Central to my research is the commitment to human-centred AI design and patient-led innovation. I actively integrate Patient and Public Involvement and Engagement (PPIE) in my work. Patients and healthcare professionals are central end users for the development process of AI-driven solutions. The integration of design innovation helps to address real-world needs, promote ethical grounding, and foster patient-centric solutions. This perspective not only enhances the relevance and empathy of AI-driven healthcare innovations, but also reinforces their adoption by establishing trust and improving usability. Through this multidisciplinary and inclusive approach, I strive to lead advancements in AI-enhanced healthcare research, combining scholarly rigour with an accessible, impact-driven vision that ultimately benefits patients and health systems.