Getting Started on Artificial Intelligence in Health Care and Clinical Research: Includes Rigor Checklist for Authors and Reviewers.
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 b...
| Publicado en: | Advances in Wound Care (2162-1918) Vol. 15; no. 1; pp. 1 - 32 |
|---|---|
| Autores principales: | , |
| Formato: | editorial pictorial tables/charts Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
Jan2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190929022&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190929022 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21621918 EGZ0 jtl: Advances in Wound Care (2162-1918) issn: 21621918 maglogo: N pubinfo: dt: Jan2026 vid: 15 iid: 1 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 190929022 190929022 190929022 10.1177/21621918251380217 190929022 ppf: 1 ppct: 31 formats: tig: atl: Getting Started on Artificial Intelligence in Health Care and Clinical Research: Includes Rigor Checklist for Authors and Reviewers. aug: au: Sen, Chandan K. DeMazumder, Deeptankar affil: McGowan Institute for Regenerative Medicine, Departments of Surgery & Cardiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. sug: subj: Artificial Intelligence Clinical Research Peer Review Checklists Health Services Medical Informatics Machine Learning Deep Learning Expert Systems Mining Natural Language Processing Robotics Automation Decision Support Systems, Clinical Workflow Pharmacovigilance Mental Health Population Surveillance Information Storage Information Retrieval Data Analytics Internet of Things ab: 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. pubtype: Academic Journal doctype: editorial pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|