Leveraging GenAI for Machine Learning Education in Public Health: ChatGPT and R

 

The world of AI is rapidly evolving — and with it, the domains that stand to benefit the most: public health, education, and healthcare research. The recently published Leveraging GenAI for Machine Learning Education in Public Health offers an intriguing blueprint for how generative AI and machine learning (ML) can be harnessed to transform public‑health training, research, and practice.

Why This Book Matters

Traditionally, applying ML to public health — disease surveillance, epidemiology, health policy, resource planning — has required deep expertise: programming skills, data‑engineering knowledge, and statistical modelling. This has often made ML inaccessible to many public‑health professionals, researchers, and policymakers who might lack a technical background.

This book bridges that gap by showing how tools like ChatGPT and other generative-AI models can be used alongside typical data-science environments to democratize ML learning. It helps build AI literacy and data-driven skillsets, even for those without prior coding experience. In doing so, it opens doors for a new generation of public-health practitioners who can leverage ML not just as a black-box tool, but as a thoughtfully applied, interpretable system for real-world health challenges.

What’s Inside the Book

The book guides readers from fundamentals to real-world applications:

• Introduction to AI and ML concepts tailored to public-health applications: classification, regression, unsupervised learning, and advanced models.

• Practical guidance on getting started with ChatGPT and RStudio, enabling “programming by prompting” and making ML more accessible.

• Use of realistic public-health datasets for hands-on practice.

• Coverage of ethical, social, and practical considerations: responsible AI use, bias mitigation, data privacy, and reproducibility.

• Real-world public-health applications and case studies demonstrating how ML can support research, interventions, and policy.

Who Should Read It

This book is especially relevant for:

• Public-health students, professionals, and researchers seeking hands-on ML skills.

• Data scientists or analysts aiming to apply ML in health contexts.

• Educators designing curricula or training programs in public health or healthcare data science.

• Policymakers and stakeholders interested in data-driven decision-making in healthcare.

• Anyone interested in how AI and ML can be responsibly leveraged for societal benefit.

Challenges and Considerations

Integrating ML and AI into public health comes with challenges:

• Data quality and bias must be carefully managed.

• Model interpretability and reproducibility are critical to avoid misuse.

• Ethical, privacy, and legal concerns must be addressed, especially with sensitive health data.

• Access and infrastructure barriers may limit adoption in some regions.

• Overreliance on AI without domain knowledge can be risky.

Hard Copy: Leveraging GenAI for Machine Learning Education in Public Health: ChatGPT and R

Kindle: Leveraging GenAI for Machine Learning Education in Public Health: ChatGPT and R

Conclusion

Leveraging GenAI for Machine Learning Education in Public Health is more than a technical guide — it’s a roadmap for bridging AI and public health in a practical, responsible way. By making ML accessible to a wider audience, it empowers professionals to make data-driven decisions, design better interventions, and improve health outcomes. For anyone interested in the intersection of AI, education, and public health, this book represents an essential resource for building knowledge, skills, and ethical awareness in the era of AI-driven healthcare.