Aug 2026· The American surgeon· pp.
31348261480830
· 0 citations· 20 references
Medicine
TL;DR
This paper presents a practical clinical framework for interrogating AI claims, organized around five core questions: What is it?
Abstract
Artificial intelligence (AI) is being integrated into clinical practice at a pace that has outstripped clinicians' ability to critically evaluate it. While 2 in 3 physicians now report using AI in some capacity in their practice, most trainees receive no formal education in AI. The result is not a deficit in intelligence or curiosity, but, at least in part, a lack of shared vocabulary that limits clinicians' ability to interpret AI claims and participate in governance decisions. This paper presents a practical clinical framework for interrogating AI claims, organized around five core questions: What is it? Does it work? Will it work here? What does it do? Is it real? These domains provide a structured approach applicable across clinical encounters, including literature appraisal, vendor evaluation, and point-of-care use. To support this framework, we define 6 vocabulary clusters commonly encountered in clinical AI discourse and introduce a translation layer that maps AI terminology to familiar traditional clinical and biological concepts. Finally, we illustrate the framework through representative real-world scenarios. This approach enables clinicians to critically evaluate AI tools, engage meaningfully in institutional decision-making, and apply consistent standards of evidence to emerging technologies.
This article proposes 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.
Alaa Abdelqader, M. Alkhateeb, Abdullah Al-Marrawi et al.· Avicenna Journal of Medicine· 0 citations
Artificial intelligence (AI) has become one of the most actively discussed tools in modern day radiology, promising to help interpret X-rays, CT scans, and MRIs alongside human radiologists. It has matched or exceeded human accuracy outright, particularly in a few narrow tasks. Furthermore, AI has come at a time when i...
Background and Objectives
The worthiness of a medical article is not known until it is read and critically evaluated. A process using artificial intelligence (AI) was developed and assessed to do that for a specific article.
Methods
A medical article was uploaded to an AI large language model program with instruction...
Douglas E. Ott· Jsls-journal of The Society...· 0 citations
BACKGROUND
Artificial intelligence (AI) is thought by many to likely underpin the next revolution in clinical medicine. The rapidly evolving terminology in AI - including terms such as 'generative AI', 'machine learning' and 'natural language processing' - can present a barrier to clinical adoption.
OBJECTIVE
The aim...
Taylan Gurgenci, Phillip Good, David Amoateng et al.· Australian Journal of Genera...· 0 citations
Bedside use of artificial intelligence (AI) platforms is increasingly common. Busy clinicians may welcome these generative AI (Gen AI) tools, which have the potential to streamline many time-consuming tasks and aid in patient care. Trainees may find them useful to quickly evaluate complex medical information. Acceptanc...
K. MacDuffie, Douglas S. Diekema, J. Kett et al.· Pediatrics· 0 citations
Cognitive safety is proposed here as a longitudinal property of the clinician-AI-organization sociotechnical system: its capacity to support or improve clinical performance without eroding independent hypothesis generation, uncertainty calibration, reasoned dissent, metacognitive control, and resilient performance when...
S. Corrao· Recenti progressi in medicin...· 0 citations
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