Artificial intelligence for early diagnosis: Md Habibur Rahman’s leading role in health innovation

In the rapidly changing landscape of technology and research, Bangladesh has produced a new generation of scholars whose work is beginning to resonate internationally.

Among them, Md Habibur Rahman, a doctoral researcher at the International American University (USA), stands out for his pioneering contributions to artificial intelligence (AI), machine learning, business analysis, and public health applications.

His career reflects how the fusion of data-driven innovation and social relevance can create transformative solutions in multiple sectors, particularly the fintech industry and healthcare systems.

In an era where technology is increasingly called upon to solve humanity’s greatest challenges, Md Habibur Rahman, a doctoral researcher at the International American University (USA), has emerged as a dynamic scholar at the intersection of artificial intelligence, machine learning and public health innovation.

With a growing body of work spanning healthcare, business analytics, and environmental health, Rahman is positioning himself as a researcher whose contributions extend well beyond academic debates into the pressing realities of disease detection, patient care, and sustainable well-being.

Rahman’s most recent scholarship is distinguished by its focus on healthcare applications of artificial intelligence, where machine learning is no longer a futuristic concept but a practical tool for saving lives.

His study on A Predictive AI Framework for Cardiovascular Disease Screening in the US integrated electronic health record (EHR) data with deep learning models to improve the early diagnosis of one of the world’s deadliest health threats.

By demonstrating how AI can identify patterns invisible to human clinicians, his research points toward a future in which healthcare becomes more predictive, personalised, and preventative.

Similarly, Rahman’s study on Artificial Intelligence for Chronic Kidney Disease Risk Stratification explored ensemble learning versus deep learning models to detect risks earlier and with greater accuracy.

Chronic kidney disease is a silent epidemic, and his contribution underscores how AI can be deployed to target underdiagnosed conditions before they become fatal. He has also contributed to the critical domain of diabetic retinopathy detection, a leading cause of blindness worldwide. His work, Advancing Diabetic Retinopathy Detection with AI and Deep Learning, not only reviewed the opportunities and limitations but also provided a roadmap for integrating AI systems into clinical practice.

Together, these studies illustrate Rahman’s deep commitment to tackling non-communicable diseases with tools that enhance both accuracy and accessibility.

Rahman’s contributions are not confined to diagnostics alone. His research on Integrating Artificial Intelligence and Data Science for Breakthroughs in Drug Development and Genetic Biomarker Discovery highlights how AI can accelerate drug discovery pipelines and unlock personalised medicine.

This frontier research has the potential to reshape the responses of pharmaceutical companies and healthcare systems to emerging health crises.

Moreover, his interdisciplinary outlook is evident in his paper, Health at Risk: Respiratory, Cardiovascular, and Neurological Impacts of Air Pollution. By linking environmental risks with human health outcomes, Rahman demonstrates a rare ability to merge computational analysis with public health policy priorities.

His work sends a clear message: technological progress must always align with the broader mission of safeguarding human lives and the environment. Although his latest contributions are firmly rooted in healthcare, Rahman’s earlier work in fintech and customer analytics provides important foundations for his research trajectory.

His papers on predicting customer sentiment in social media interactions and enhancing customer satisfaction in fintech industries showcase his skill in applying AI to optimise decision-making, improve experiences, and strengthen resilience in data-driven industries.

These dual contributions to business intelligence and healthcare innovation give him a unique interdisciplinary identity that sets him apart in the global research landscape.