Skip to content
Open access

The Promises and Perils of Clinical Decision Support Artificial Intelligence

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.

Read PDF

Similar papers

2026

AI for Personalized Medicine and Clinical Decision Support

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...

Surabhi Shankera, Saloni Bhushanb, Indu Balac · 0 citations
Sep 2026

Evaluating the Effectiveness of Artificial Intelligence–Based Clinical Decision Support in Improving Early Disease Detection and Patient Care Outcomes

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 · 0 citations
Editorial Open access Aug 2026

Ask-Audit-Apply: A Framework for Clinical Reasoning with Artificial Intelligence.

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 · 0 citations
#large language models Review Open access Sep 2026

Advances, Risks, And Implementation Challenges of Artificial Intelligence as A Force Multiplier for Clinical Decision Making and Health System Efficiency

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 · 1 citation
Review Open access Sep 2026

On-premise medical AI agents for reliable clinical decision-making.

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. · 2 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.