Getting Started on Artificial Intelligence in Health Care and Clinical Research: Includes Rigor Checklist for Authors and Reviewers

Abstract

Artificial intelligence (AI) is rapidly transforming biomedical research and health care, offering new paradigms for discovery, diagnosis, and decision-making. This article provides a roadmap for researchers, clinicians, and reviewers seeking to understand and apply AI with rigor and relevance. It begins with a historical anchor: the birth of AI in health care at the University of Pittsburgh in the 1970s, where the INTERNIST-1 system pioneered diagnostic reasoning through symbolic logic, a milestone that laid the foundation for today's intelligent systems. Structured into three tiers-foundations, core techniques, and applications-the article addresses the full spectrum of biomedical AI. It introduces foundational concepts such as data engineering and preprocessing, knowledge representation and reasoning, and symbolic AI, which together enable structured, interpretable intelligence. Core techniques including expert systems, machine learning, deep learning, and explainable AI are presented with clinical examples, highlighting their role in wound care, image analysis, and predictive modeling. The applications tier showcases natural language processing, non-machine learning computer vision, robotics and automation, and distributed AI/multi-agent systems, demonstrating how AI integrates into real-world workflows. Ethical considerations and bias mitigation strategies are addressed with emphasis on Institutional Review Board oversight and fairness frameworks. Crucially, the article emphasizes that successful AI adoption begins not with technology, but with people. It outlines a systematic approach to building a biomedical AI workforce from within, empowering clinicians, researchers, and staff to become AI-literate contributors and leaders. With rigor checklists, practical guidance, and a vision for human-AI collaboration, this article invites readers to move beyond hype and toward responsible, transformative innovation in health care and biomedical science. [Figure: see text].

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