By Staff Reporter
SM Toufiqur Rahman holds a Doctor of Veterinary Medicine (DVM) from Patuakhali Science and Technology University, Bangladesh. He is pursuing master's studies in Business Administration and Management at MIU City University Miami, Florida. His professional background includes veterinary service at Ad-din Welfare Centre and more than four years in SQUARE Pharmaceuticals PLC's AgroVet Division. His research interests bring together artificial intelligence (AI), agribusiness analytics, food safety and One Health.

Q: Why do AI, food safety and One Health belong in the same global conversation?
Rahman: Food safety begins before food reaches consumers. Animal health, farm practices, environmental conditions, processing and distribution are interconnected. One Health encourages us to examine these relationships together. AI can help specialists recognize patterns, prioritize investigations and identify risks earlier. However, technology must support laboratory testing and professional judgment, not replace them. Because food moves across borders, stronger prevention in one country can protect people elsewhere.

Q: How has your veterinary experience shaped this perspective?
Rahman: My veterinary education, farm-level work at Ad-din Welfare Centre and industry experience at SQUARE have shown me the importance of decisions. Behind every dataset are farmers, veterinarians, laboratory personnel and managers who must respond. A prediction is only valuable when people understand it and can take action. That is why I believe research should begin with operational problems.

Q: What AI application would you like to investigate?
Rahman: I would be interested in testing whether combining laboratory findings, animal-health reports and food-storage records could help identify risks requiring examination. Researchers could compare AI-assisted alerts against inspection methods, measuring missed hazards, false warnings and response times. This is a potential research project, not a system I claim to have implemented. Any findings would need validation before practical use.

Q: Why should environmental conditions be considered?
Rahman: Weather, water quality and temperatures can influence risks throughout food production and distribution. Their effects may differ between regions. I would like to investigate whether incorporating environmental information improves risk assessment. The objective should not be collecting more data for its own sake, but determining whether those data help professionals make safer decisions.

Q: How could this work benefit countries with different resources?
Rahman: A single model will not suit every country. Production methods, infrastructure, connectivity and available expertise vary. Solutions should be affordable, understandable and tested locally. Small-scale producers must be included in design and evaluation. I believe international research should strengthen local skills rather than create dependence on technology that communities cannot maintain.

Q: What kind of international collaboration would make the greatest difference?
Rahman: Veterinary services, food-safety authorities, researchers and producers should share methods, conduct joint validation studies and establish reporting procedures. Collaboration also requires safeguards for information and agreement about who responds when a risk is detected. Countries can learn from each other without assuming their circumstances are identical. The goal is stronger local capacity supported by shared evidence.

Q: Where does agribusiness management fit into this research?
Rahman: Even technology can fail without management. Organizations must decide who funds a system, maintains it, trains staff and acts on warnings. My interests in predictive analytics and agribusiness supply chains encourage me to study those implementation questions alongside technical performance. Effective food safety requires both reliable scientific information and workable institutional processes.

Q: What safeguards are essential before relying on AI?
Rahman: AI systems need dependable data, limitations, testing in relevant settings and human oversight. A warning should trigger investigation rather than automatic conclusions. Equally, the absence of a warning must never be treated as proof of safety. Qualified professionals and accountable institutions should remain responsible for decisions affecting health and livelihoods.

Q: How would you assess whether such research succeeds?
Rahman: I would measure detection accuracy, missed risks, false alerts, response time, operating costs and whether people continue using the system. Reduced foodborne illness would require additional evidence; it cannot be inferred from model accuracy. I would examine whether smaller producers and less-resourced agencies can benefit. Global progress should be measured by practical outcomes, not technology alone.

Q: What contribution do you hope to make?
Rahman: I hope to help develop and evaluate approaches that connect veterinary knowledge, AI and management research. My aim is not to promote one universal solution, but to generate methods that countries can test, adapt and improve. I would like this work to support earlier prevention, better coordination and safer food systems for people worldwide.

Biography
SM Toufiqur Rahman is pursuing master's studies at MIU City University Miami. His experience spans veterinary practice and the animal-health industry. His research interests include AI, agribusiness analytics, food safety and One Health.
Email: drtsrahman@gmail.com