Aug 2026· The Clinical Teacher· Vol 23· 0 citations· 25 references
Medicine
TL;DR
A viewpoint of AI tools for clinical decision support is presented, addressing a rapidly evolving field and provides insights that are useful for clinicians and educators in clinical settings.
Abstract
Evidence‐based clinical decision support artificial intelligence (AI) is rapidly expanding, but its safe and effective use depends on rigorous validation, trustworthy evidence sources and careful integration into clinical workflows. Current available systems show strong potential to improve diagnostic accuracy, reduce clinician workload and possibly benefit patient care, but challenges remain before its real‐world adoption. We must be responsible in its integration to ensure AI truly strengthens clinical judgment. This article is a viewpoint of AI tools for clinical decision support, addressing a rapidly evolving field and provides insights that are useful for clinicians and educators in clinical settings. When used within appropriate medical training, AI may help augment diagnostic accuracy and improve efficiency. Nevertheless, while AI offers promising features, it also presents ethical and reliability challenges, which may negatively affect the medical professional identity.
To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required.
Eugenio Santoro· Recenti progressi in medicin...· 0 citations
With rapidly evolving healthcare, artificial intelligence (AI) is an emerging tool of the healthcare environment and personalized medicine. AI can read large volumes of data about patients, such as genetic profile, medical imaging, demographics, and lifestyle factors, faster and more accurately than ever before using a...
Abstract
Background: Artificial Intelligence (AI)–based Clinical Decision Support (CDS) systems have emerged as transformative tools in modern healthcare, offering the potential to augment clinical reasoning, enhance diagnostic accuracy, and accelerate early disease detection. However, evaluating their real-world clini...
Mahendra Pratap Swain· The Journal of Integrated Re...· 0 citations
Ask-Audit-Apply (AAA) is proposed as a clinician-facing framework for AI-supported clinical reasoning that specifies decision-level behavioral practices that may support education, supervision, and future research on safe AI integration across a range of CDS tools as these systems increasingly participate in clinical c...
Andrew Jenkins, Katherine W. Eisenberg, R. Ziegelstein· Journal of general internal...· 0 citations
The central argument is that the value of clinical artificial intelligence is determined less by model accuracy in isolation than by the quality of the sociotechnical system into which a model is placed: governance, workflow integration, monitoring, and the preservation of meaningful human oversight.
Lucky Ilodigwe· International Journal of Med...· 1 citation
A fully on-premise clinical agent that couples local operational control with a multi-perspective reliability framework to support selective autonomy is developed and evaluated, supporting a practical framework for institutionally governed clinical agents.
Li Zhang, G. Wölflein, Dyke Ferber et al.· Nature Medicine· 2 citations
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