From Algorithm to Bedside: A Clinician's Framework for AI in Practice.

Authors

Alaa Abdelqader, Mohammad Alkhateeb, Abdullah Al-Marrawi, Hadi Baianonie, Fares Alahdab

Published in

Avicenna journal of medicine. Volume 16. Issue 3. Pages 101-106. Epub Aug 12, 2026.

Abstract

Artificial intelligence (AI) is entering clinical practice faster than the evidence base supporting it. Clinicians, who remain the licensed decision-makers at the bedside, increasingly find themselves as end-users of tools whose strengths, failure modes, and external validity they have had no opportunity to assess. This article offers a practical framework that does not require coding or mathematical literacy. We outline how AI is built, validated, deployed, and monitored, and where each phase typically goes wrong. We propose seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions. We then examine the deeper questions of equity, accountability, and the therapeutic relationship that AI is now forcing into view. AI literacy belongs alongside biostatistics and evidence-based medicine as a core clinical competency.

PMID:
42719538
Bibliographic data and abstract were imported from PubMed on 10 Sep 2026.

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