Abid Hasan Khan: The United States of America is currently leading the world in AI innovations, and so are the US-based

researchers; one of the leading voices in this space is Md Ashraful Alam, a Bangladeshi researcher based in the US whose groundbreaking work is transforming the landscape of AI in national healthcare. He is currently pursuing his PhD in Computer Science at Colorado State University, with a focus on AI-powered health diagnostics and real-time public health analytics. One of his primary goals is to integrates AI, machine learning, and data security to improve diagnostic precision and build trustworthy medical AI systems that support U.S. healthcare readiness and resilience. A research paper by Md Ashraful Alam, titled “medical imaging for early cancer diagnosis and epidemiology using artificial intelligence: strengthening

national healthcare frameworks in the usa,” published in the American Journal of Scholarly Research and Innovation, is drawing attention from clinicians, health-technology leaders, and policy analysts across the United States. The study examines how artificial intelligence applied to medical imaging can strengthen early cancer diagnosis and population-level epidemiology, with direct implications for hospital workflows and public health planning. By detailing how CT, MRI, PET, and mammography data can be analyzed in real time to flag

risk earlier and guide targeted screening, the paper speaks to a core national priority: saving lives while reducing the cost and strain of late-stage treatment. It also outlines practical steps for privacy, interoperability, and equitable access—key requirements for a modern U.S. healthcare system that must deliver timely, reliable care within real infrastructure and budget limits.

Md Ashraful Alam research endeavor addresses one of the most urgent vulnerabilities in U.S. healthcare: the twin challenge of detecting disease earlier and protecting patient data. Healthcare organizations now face average breach costs exceeding $9.77 million per incident, and in the U.S., breach-related expenses recently climbed to $10.22 million due to penalties, technical response, and reputational damage (HIPAA Journal, 2025). Thousands of data breaches affecting millions of health records continue to be reported each year, exposing highly sensitive patient information and disrupting essential services (HIPAA Journal, May 2025). At the same time, chronic diseases such as cancer, diabetes, and heart conditions account for over $4 trillion in annual healthcare costs, placing a massive burden on hospitals and long-term care systems (CDC, 2024). In response, the U.S. Department of Health and Human Services has made it a strategic priority to modernize healthcare through artificial intelligence, predictive modeling, and big data frameworks (HHS, 2025).

Alam’s AI-based healthcare study titled “Medical Imaging for Early Cancer Diagnosis and Epidemiology Using Artificial Intelligence: Strengthening National Healthcare Frameworks in the USA” by Md Ashraful Alam makes a timely and focused contribution. This research explores how artificial intelligence can enhance medical imaging to support early detection of cancer. Alam’s study demonstrates how AI algorithms can identify cancerous patterns in imaging data earlier and more precisely than traditional methods. This improvement in diagnostic timing is essential to initiating timely treatment, reducing the burden of late-stage interventions, and easing pressure on the U.S. healthcare system. The paper also extends beyond individual diagnosis by exploring how AI-driven imaging can be used to detect broader epidemiological trends, helping healthcare authorities make informed decisions at the population level. His paper, “Medical Imaging for Early Cancer Diagnosis and Epidemiology Using Artificial Intelligence: Strengthening National Healthcare Frameworks in the USA,” was

cited in the Q1 article “Comparative Analysis of Neural Network Architectures for Medical Image Classification: Evaluating Performance Across Diverse Models,” published in the American Journal of Advanced Technology and Engineering Solutions. The authors used his work to frame why AI and big data matter for medical image analysis and early disease detection. They linked their comparison of CNNs, ResNets, DenseNets, Vision Transformers, and EfficientNets to the larger message in his study: integrating AI into imaging improves diagnostic accuracy, supports epidemiological monitoring, and helps modernize healthcare systems. This citation recognizes his contribution as a foundational perspective for their analysis and shows that his research is informing ongoing efforts to use AI in imaging to deliver earlier intervention and better outcomes.

Another research paper by Md Ashraful Alam, titled “The Role of Predictive Analytics in Early Disease Detection: A Data-Driven Approach to Preventive Healthcare,” published in Frontiers in Applied Engineering and Technology (AIM International LLC Publisher), 2024; 1(01): 105–123, is drawing attention from clinicians, hospital administrators, and public-health planners across the United States. The study explains how combining electronic health records, wearable and sensor streams, and demographic data can spot risk earlier, reduce emergency admissions, and guide faster clinical decisions. Using a PRISMA-guided review that screened 687 papers and synthesized 37 studies, the paper shows that data-driven models routinely achieve strong diagnostic accuracy for conditions such as diabetes, heart disease, and cancer, while real-time monitoring and early- warning systems can help patients and clinicians act before crises escalate. It also describes how forecasting during events like COVID-19 can help plan ICU beds, ventilators, and staff, turning prediction into practical resource planning that lowers costs and improves outcomes. Beyond accuracy, the work lays out clear safeguards for privacy, bias mitigation, and interoperability so these tools can be trusted and adopted across U.S. hospitals and public-health agencies. In plain terms, the paper offers a blueprint for preventive, trustworthy, and scalable analytics that protect U.S. lives, reduce national healthcare spending, and strengthen health security—meeting a core national priority of delivering timely, reliable care within real infrastructure and budget limits.

His paper, “The Role of Predictive Analytics in Early Disease Detection: A Data-Driven Approach to Preventive Healthcare,” was cited in the article “Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.” The authors used his study to introduce preventive analytics and to support their core claim that AI-driven prediction can shift care from reaction to prevention by identifying at-risk patients before symptoms appear. They wrote, “Predictive analytics powered by AI offers a proactive approach to disease prevention by identifying health risks before clinical symptoms emerge [38],” where reference [38] is his article. In their references, his work is listed as “Alam MA, et al. The role of predictive analytics in early disease detection: a data-driven approach to preventive healthcare. 2024;1(01):105–23.” This citation shows that his research is being recognized as evidence that combining clinical records and wearable signals can flag high-risk patients early, and it strengthens the broader message that AI enables earlier intervention, reduces downstream costs, and supports preventive care in the United States and beyond.

This research presents a transformative framework for cancer epidemiology, leveraging artificial intelligence to detect cancer early and enhance real-time health surveillance. As cancer remains one of the most serious public health challenges in the U.S., timely and accurate diagnosis is crucial for improving patient outcomes, optimizing resource use, and reducing national healthcare costs. By applying this AI-powered imaging pipeline—this study offers a unique blueprint for modernizing the American healthcare systemThis work not only advances medical imaging but also highlights the urgent need to implement AI-driven epidemiological tools in order to safeguard the future of US national healthcare.

This research directly aligns with US national healthcare priorities, especially those aimed at modernizing infrastructure and addressing chronic disease burdens. By focusing on AI integration into diagnostic imaging and its application in public health planning, Alam’s work provides practical direction for improving health outcomes while optimizing national resources. The study offers a real-world model that demonstrates how AI-driven imaging can contribute to the U.S. healthcare system by supporting earlier interventions, lowering treatment costs, and helping health systems respond more effectively to rising cancer rates. Through this contribution, Alam's research reinforces the role of AI as a powerful tool for building stronger, more responsive national healthcare frameworks. “As artificial intelligence continues to make strides in the field of healthcare, its applications are transforming traditional approaches to diagnosis and public health monitoring.

Another paper of Alam’s, “Real-Time Analytics in Streaming Big Data: Techniques and Applications,” published in the Journal of Science and Engineering Research, Vol. 1, No. 1 (November 2024), pages 104–122, , is gaining attention from data engineers, clinicians, and public-health planners for showing how streaming pipelines can turn live data into faster, better decisions. He reviewed 160 peer-reviewed studies and explained how technologies like Apache Kafka, Flink, and Spark Streaming can ingest continuous signals from EHRs and wearables, detect high-risk events such as cardiac issues, sepsis, and diabetic emergencies in near real time, and trigger timely alerts that reduce avoidable admissions and costs. He also showed how forecasting on streaming data helps hospitals plan ICU beds, ventilators, staff, and supplies before peaks hit, a lesson strengthened by COVID-19 operations. The paper pairs technical guidance with guardrails for privacy, bias mitigation, and interoperability, so health systems can adopt these tools with trust. In short, this work is being recognized as a practical blueprint for building preventive, scalable, and resilient analytics that save lives, lower spending, and strengthen healthcare readiness across the United States. Md Ashraful Alam’s paper “Real-Time Analytics in Streaming Big Data: Techniques and Applications” is cited as reference [53] in the IEEE Internet of Things Journal article “Bibliometric Analysis of Digital Twin: Scientometrics and Future Research Trends.” The citation appears in passages that explain how digital twins operate continuously in real time, noting that cloud platforms such as Microsoft Azure can handle large-scale computation while big-data and streaming analytics enable low- latency evaluation. By placing [53] after statements about cloud-based heavy processing and again alongside technologies like Apache Hadoop, the authors use Alam’s work as evidence that combining cloud compute, distributed storage, and streaming pipelines supports constant data ingestion, rapid processing, and timely feedback to keep virtual models synchronized with physical systems at scale.

Md Ashraful Alam also shared his insights, motivations, and experiences in a conversation. When asked about his research direction, he explained, “I am mainly focusing on U.S. healthcare through AI & data Science to enhance diagnostic precision and protecting sensitive medical data. His work aims to reduce hospitalization rates and improve real-time patient monitoring by leveraging data from EHR systems, wearable sensors, and large- scale public health datasets. Alam emphasized, “AI is not just a tool—it’s a responsibility. We must train it with the right ethics and purpose, so it helps clinicians, protects privacy, and truly saves lives.” He believes the best way to use AI is not just to automate tasks but to empower healthcare providers with better foresight, transparency, and trust in decision-making. By designing AI-powered medical imaging systems embedded within secure, privacy-conscious platforms, Alam’s aim to support earlier diagnosis, reduce treatment costs, and strengthen U.S. healthcare infrastructure. This research aligns directly with federal modernization goals and represents a step toward a safer, more preventive, and data-resilient healthcare system.

The writter: Abid Hasan Khan,  Freelancer Writer University of Dhaka