Abstract
Healthcare systems globally suffer from a shortage of qualified staff and resources with the problem being really acute in low- and middle-income countries (LMICs) like Pakistan. Advances in Artificial Intelligence over the past decade have the potential to improve efficiency; however, a number of challenges must be overcome before (and after) clinical deployment to ensure that patients and clinicians truly benefit from this technology. Financial constraints, country specific clinical protocols, cultural sensitivities of patients and locally sourced data need to be taken into account during the design process to ensure successful deployment and sustainability.
In this seminar, Dr. Hassan Aqeel Khan will share state-of-the-art AI tools that are being built to ameliorate problems facing resource-constrained healthcare systems in Pakistan and the UK. In particular, he will talk about research and data collection efforts related to AI-enabled neurology, computational pathology and radiology and how these feed into healthcare problems specific to Pakistan. He will demonstrate how eye tracking technology is being used for prompt generation of data annotations that are then used to train AI algorithms. Furthermore, he will also share valuable lessons learned from 10 years of doing healthcare-AI research in Pakistan and how we are working with local collaborators to develop indigenous solutions for local problems.
Speakers
Dr. Hassan Aqeel Khan is a Senior Lecturer in Applied AI and Robotics at Aston University Birmingham UK. His primary area of research is applications of Machine Learning and AI in Medical Imaging and Biomedical signal analysis. Hassan is currently leading a £200,000/-, EPSRC funded, grant related to the development of Explainable AI (XAI) solutions for neurological data using large language models (LLMs). Throughout his career, he has been involved in grants worth over £320,000/- as PI or CoI. His research projects have been funded by the EPSRC, German Academic Exchange Service (DAAD), Nokia Research Centre China, Deanship of Scientific Research at the University of Jeddah Saudi Arabia, and Higher Education Commission (HEC) Pakistan. Hassan’s recent research focuses on novel data annotation strategies for efficient labelling of large volumes of training data for computational pathology and neurology data. Hassan has a PhD in Electrical Engineering (2015) from Michigan State University, an MSc (with Distinction) in Signal Processing and Communication (2007) from the University of Edinburgh and BSc in Electrical Engineering (2003) from UET Taxila Pakistan.
Collaborators
