Preface

This volume is the fourth in a graduate sequence on quantitative methods for clinical and public-health research. The first three volumes cover the methods and infrastructure of research practice: introductory and advanced statistical computing, plus a practicum on the workflow tools that surround them. This fourth volume covers the orthogonal axis that has reshaped applied analytic work between 2023 and 2026: the use of generative AI as a working collaborator.

The intended audience is the graduate student or working researcher in biostatistics, clinical research, or public health who runs the analyses and signs the reports. Throughout the book, the researcher refers to whoever sits in that seat regardless of disciplinary label. The book assumes familiarity with statistical methods at the level of a first-year graduate sequence and fluency in R at the level of R for Data Science. It does not assume prior experience with large language models, agents, or multimodal AI; those are introduced from the ground up, oriented to the research use cases the audience will encounter.

What this book covers

The 13-chapter structure is organised in five parts:

  1. Foundations: a brief genealogy of generative AI; the contemporary landscape and capability classes; reasoning models, context, and the verification problem.
  2. Working with biomedical knowledge and data: retrieval-augmented generation over biomedical corpora; synthetic data with privacy guarantees; multimodal medical AI.
  3. Agentic workflows and tool use: agents, tool use, and the Model Context Protocol; deep research and evidence synthesis pipelines.
  4. Evaluation, safety, and governance: evaluation beyond the benchmark; safety, bias, and red-teaming; regulation, privacy, and the IRB.
  5. Customisation, deployment, and practice: fine-tuning, distillation, and AI-augmented teams; deploying AI in clinical and public-health practice.

The chapter list was constructed by surveying current graduate-level applied-AI curricula across major US biostatistics, public-health, and adjacent programmes (the survey is documented in docs/syllabi-survey.md) and cross-checking the result against a 24-month digest of Ethan Mollick’s One Useful Thing Substack (docs/mollick-digest.md), a touchstone for contemporary applied genAI thinking. The result is a curriculum that reflects mainstream 2026 practice while staying anchored to clinical and public-health research use cases.

What this book does not cover

The book deliberately omits topics that are taught elsewhere in the sequence or in dedicated courses:

  • The mechanics of training large language models from scratch, including pre-training and reinforcement learning from human feedback. We treat models as capability tiers to be selected and verified, not artefacts to be trained.
  • Classical machine learning, which is treated in Advanced Statistical Computing in the Age of AI, Chapter 10.
  • General software engineering for statisticians, which is treated in Advanced Statistical Computing in the Age of AI, Chapter 11.

Pointers to relevant chapters appear where they arise.

Three load-bearing concepts

Three concepts thread through every chapter and earn the ‘applied’ framing.

The jagged frontier. Generative AI capability is uneven: superhuman in some dimensions, subhuman in adjacent ones. The frontier is also opaque, capability and verification difficulty grow together. The researcher’s contribution is to identify which jagged edges still require human judgement and which do not, and to design verification proportionate to the stakes.

The cybernetic teammate. Modern AI assistance is more fruitfully framed as collaboration with a teammate than as operation of a tool. The 2024–2026 arc has shifted the human role from prompter to manager: the binding skill is precise specification of deliverables, fast evaluation, and judicious delegation. This framing organises the book from Chapter 1 forward.

Verification as a first-class concern. Every chapter treats verification not as a final step but as a design property. The section template in every chapter front-loads The researcher’s contribution, the judgements no AI can make on the reader’s behalf, and ends with Collaborating with an LLM, where specific prompts are paired with what to watch for and how to verify. The dialectic structures the chapter rather than decorating it.

How to read this book

Each content chapter follows the same structure: Learning objectives, Orientation, The researcher’s contribution, content sections (with collapsible Check-your-understanding callouts at natural pauses), Worked example, Collaborating with an LLM, Principle in use, Exercises, Further reading.

Chapters can be read in order or out of order. Topics with a chain of dependencies (e.g., Chapter 7 builds on Chapter 3; Chapter 13 builds on Chapter 12) are noted in the relevant Orientation sections.

Acknowledgements

The chapter list reflects a survey of graduate-level genAI curricula across roughly twenty US programmes and several industry-adjacent open courses. Ethan Mollick’s One Useful Thing Substack provided the contemporary frame against which the chapter list was tested. The authors of R for Data Science, Advanced R, R Packages, Bayesian Data Analysis, and Statistical Rethinking established conventions this book inherits from its sister volumes.