Dr. Zubair Shah
Program Lead
Associate Professor, College of Science and Engineering, Hamad Bin Khalifa University
2019–present
About
Dr. Zubair Shah leads DAQIQ. He works on artificial intelligence for health, including large language and foundation models, multimodal AI that combines imaging, health records and omics data, and precision medicine for diabetes.
He joined HBKU in 2019 and coordinates the Master of Information Systems in Health Management program. Before HBKU he was a Research Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University. He holds a PhD in Computer Science from UNSW Canberra.
Research focus: AI for health, multimodal AI and precision diabetes
Projects
Project lead, Principal Investigator · 2026–2032ActiveAI-driven healthcare surveillance and proactive care recommendation in smart environments
Project lead, Principal Investigator · 2026–2027ActiveLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
PhD supervisor · 2022–2026CompletedHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
PhD supervisor · 2021–2024CompletedAain-D: AI for diabetic retinopathy from retinal images
Project lead, Principal Investigator · 2022–2025CompletedC.P.-Support: peer support for people living with diabetes
Project lead, Principal Investigator · 2020Completed
Talks
Publications with DAQIQ 17
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Artificial intelligence–based diabetes risk prediction from longitudinal DXA bone measurements
Khan S, Shah Z
Scientific Reports 15: 25706, 2025
Journal articleLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
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Khan S, Biswas MR, Shah Z
Artificial Intelligence in Healthcare (AIiH 2025), Lecture Notes in Computer Science 16038, Springer, 2025
Conference paperTalk and slidesLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
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Early prediction of type 2 diabetes using multimodal data and tabular transformers
Khan S, Biswas MR, Shah Z
3rd International Conference on Foundation and Large Language Models (FLLM 2025): 592–599, IEEE, 2025
Conference paperFree versionTalk and slidesLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
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ECG features improve multimodal deep learning prediction of incident T2DM in a Middle Eastern cohort
Mohsen F, Safa A, Shah Z
Scientific Reports 15: 27164, 2025
Journal articleHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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Integrating non-linear Radon transformation for diabetic retinopathy grading
Mohsen F, Belhaouari S, Shah Z
Scientific Reports 15: 30706, 2025
Journal articleAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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KEM-IoMT: knowledge graph embedding-enhanced accurate medical service recommendation against diabetes
Khan N, Mufti MR, Arif M, Ali A, Shah Z
Computers in Biology and Medicine 194: 110463, 2025
Journal articleAI-driven healthcare surveillance and proactive care recommendation in smart environments
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Khan S, Shah Z
IEEE International Conference on Information Reuse and Integration for Data Science (IRI 2025): 289–294, 2025
Conference paperTalk and slidesLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
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RadEx: an open source Python package for nonlinear Radon transformation
Mohsen F, Islam A, Mohsen F, Shah Z, Belhaouari SB
Software Impacts: 100779, 2025
Journal articleAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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A comparative evaluation of artificial intelligence-based models in retinal image synthesis
Mohsen F, Aldhubri A, Biswas MR, Shah Z
2nd International Conference on Foundation and Large Language Models (FLLM 2024): 51–57, 2024
Conference paperTalk and slidesAain-D: AI for diabetic retinopathy from retinal images
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Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review
Khan S, Mohsen F, Shah Z
Artificial Intelligence Review 58(2): 41, 2024
Journal articleLongitudinal analysis of risk factors associated with diabetes using multimodal data and machine learning
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Introducing RadEx: adaptive parameterized feature extraction from medical images
Islam A, Mohsen F, Shah Z, Belhaouari SB
Computer Graphics International Conference (CGI 2024): 278–294, Springer, 2024
Conference paperTalk and slidesAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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Multimodal deep learning for diabetic retinopathy grading: integrating linear-Radon sinograms and retinal fundus images
Mohsen F, Shah U, Islam A, Shah Z, Belhaouari SB
IEEE 26th International Workshop on Multimedia Signal Processing (MMSP 2024): 1–5, 2024
Conference paperTalk and slidesAain-D: AI for diabetic retinopathy from retinal imagesHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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A scoping review of artificial intelligence-based methods for diabetes risk prediction
Mohsen F, Al-Absi HRH, Yousri NA, El Hajj N, Shah Z
npj Digital Medicine 6: 197, 2023
Journal articleHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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Predicting long-term type 2 diabetes with artificial intelligence (AI): a scoping review
Sonko S, Lamya F, Alzubaidi M, Shah H, Alam T, Shah Z, Househ M
Healthcare Transformation with Informatics and Artificial Intelligence (ICIMTH 2023): 652–655, IOS Press, 2023
Short paper
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Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen F, Ali H, El Hajj N, Shah Z
Scientific Reports 12: 17981, 2022
Journal articleHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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Customized and automated machine learning-based models for diabetes type 2 classification
Mohsen F, Biswas MR, Ali H, Alam T, Househ M, Shah Z
Advances in Informatics, Management and Technology in Healthcare: 517–520, IOS Press, 2022
Short paperHarnessing the power of artificial intelligence: predicting type 2 diabetes onset
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Understanding the food habits and physical activities of diabetes cohort in Qatar
Nazeemudeen A, Al-Absi HRH, Refaee MA, Househ M, Shah Z, Alam T
The Importance of Health Informatics in Public Health during a Pandemic: 453–456, IOS Press, 2020
Short paper