Applying AI to Facilitate Clinical Research & Peer Review Workflows: A Recipe for Effective, Efficient, and Ethical Practice Learning Lab

Modern AI has the potential to dramatically accelerate clinical research and peer review—but when used imprecisely or without safeguards, it can introduce ethical risks, reproducibility gaps, and credibility issues. Many clinicians and researchers are already experimenting with AI tools, yet lack clear guidance on when to use them, how to use them responsibly, and how to document their use transparently. This learning lab addresses that gap by grounding AI use in ethical, privacy-preserving, and reproducible research practices that fit real clinical environments. 

Attendees learn how to responsibly apply modern AI across the entire research lifecycle, from ideation and literature review through study design, analysis, manuscript preparation, presentation, and peer review. Participants gain practical, no-/low-code workflows they can run on everyday laptops or mobile devices—without needing a technical background. Throughout the session, attendees practice using tested prompts, guardrails, and checklists to avoid common pitfalls such as hallucinations, confidentiality breaches, and prompt-injection risks, while improving efficiency and clarity.  

The lab blends short interactive explanations with live demonstrations and participant-driven exploration. Attendees engage in polling, micro-demos, and small-team challenges that mirror real research scenarios, including literature screening, protocol drafting, visualization, and reviewer response planning. Participants leave with a take-home packet containing reusable prompts, ethical do/don’t checklists, documentation templates, and a reproducible “AI-for-Research” workflow they can immediately adapt to their own work. 

Objectives

• Identify appropriate and ethical opportunities to use AI across each stage of clinical research and peer review. 
• Apply tested prompting strategies, guardrails, and checklists to reduce errors, bias, and confidentiality risks when using AI tools. 
• Use no-/low-code AI tools to support literature review, study design, analysis, manuscript preparation, and presentation development. 
• Practice executing an end-to-end AI-supported research workflow using realistic clinical research scenarios. 
• Create a research artifact (e.g., literature summary, protocol draft, figure, abstract, or review plan) using ethically guided AI assistance. 
• Implement transparency and reproducibility documentation that clearly describes the role of AI in research and peer review activities.