Preface
I wrote this handbook because the constructive side of AI in biology deserves a practical, evidence-centered home. Clinical AI, public health AI, and biosecurity each answer different questions. This book focuses on discovery and engineering: molecules, cells, experiments, therapeutics, and the research systems that connect them.
The goal is not to rank every model or chase every release. The goal is to give researchers a stable way to read claims, design evaluations, and decide when an AI output deserves experimental attention.
Bryan Tegomoh, MD, MPH is a physician-scientist and epidemiologist. His work spans biomedical research, public health, biosecurity, and AI evaluation.
How the Book Uses Evidence
The handbook uses three evidence tiers throughout:
- Demonstrated: supported by published evidence, official documentation, or reproducible benchmark results.
- Theoretical: plausible based on current methods, but not yet established for routine use.
- Beyond current capabilities: not supported by credible evidence with current systems.
Claims about AI systems in biology are placed inside those tiers so readers can separate current utility from research aspiration.