Bridging the last mile in AI-driven patient safety - the missing link of trainee readiness

Sep 29, 2026

Artificial intelligence is transforming patient safety through early diagnostics, constant monitoring, and clinical decision support, yet medical trainees often lack preparedness to use AI tools responsibly in day-to-day practice. AI-based early warning systems can detect deterioration hours before it becomes obvious, and evidence suggests meaningful outcome benefits. However, trainees’ limited exposure, low confidence, and gaps in trust and interpretability training hinder effective alert interpretation and action. A reform in education—simulation, collaboration, and mandatory digital health modules—is proposed to close this last-mile gap.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

Introduction

Main content

AI-based early warning systems and outcomes

The human side: trainees interpreting alerts

Educational gap and proposed solutions

Conclusion

Letter to the Editor Bridging the last mile in AI-driven patient safety: the missing link of trainee readiness Rafay Ullah Khan, MBBSa , Nabiah Shakeel, MBBSa , Mohammed Hammad Jaber Amin, MBBSb ,* Introduction Artificial intelligence (AI) has rapidly emerged as a game-changer in modern healthcare, particularly in enhancing patient safety through early diagnostics, constant monitoring, and clinical decision support. However, a considerable gap still exists despite all the advancements in technology and the lack of preparedness of medical trainees to engage effectively with AI tools in clinical practice. This issue is increasingly urgent as healthcare institutions adopt AI-driven systems faster than educational frameworks can prepare future clinicians to use them ina responsible manner. The article is compliant with the TITAN Guidelines 2025 – governing declaration and use of AI[1] . Main content AI-based early warning systems (AI-EWS) are changing how we recognize patient deterioration in hospitals. These systems can detect subtle physiological changes several hours before they become clinically obvious [2] . A recent meta-analysis that included data from multiple countries found that AI-EWS reduced unplanned ICU transfers and inpatient deaths by approximately 23%[3] . While these numbers are encouraging, the global use of AI-EWS around the world is still very different from each other. The key point of the whole discussion until now has focused on how accurate these systems are or how smoothly they fit into hospital workflows. However, less attention is given to the human side; the people expected to interpret and act on these alerts. In most hospitals, trainees such as medical students, interns, and nurses are often the first to notice early signs of deterioration. Yet, many of them have little exposure to AI systems or confidence in using their alerts [4] . Early signs aDepartment of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan and bDepartment of Medicine, AlzaiemAlazhari University, Khartoum, Sudan Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. *Corresponding author. Address: Department of Medicine, AlzaiemAlazhari University, 123 Alkalakla Khartoum, Khartoum 11111, Sudan. Tel.: +[249119303629. E-mail: mohammesjaber123@gmail.com](249119303629. E-mail: mohammesjaber123@gmail.com)[ ](249119303629. E-mail: mohammesjaber123@gmail.com)(M.H. Jaber Amin). Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the terms of the Creative Commons AttributionNon Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Annals of Medicine & Surgery (2026) 88:1104–1105 Received 10 November 2025; Accepted 19 November 2025 Published online 3 December 2025 [http://dx.doi.org/10.1097/MS9.0000000000004467](http://dx.doi.org/10.1097/MS9.0000000000004467) such as subtle respiratory distress or mild confusion can be missed until the situation becomes critical. Introducing AI tools without preparing trainees to use them properly may simply replace one type of safety gap with another. Many studies have also pointed out that trust and interpretability are central to whether clinicians actually use AI tools effectively, but these topics are rarely part of undergraduate or nursing education [5] . In many academic institutions, AI-related topics are restricted to brief lectures or optional workshops instead of getting full and hands-on learning experiences. This creates a gap between the technological development of AI and its application to clinical practice. A solution to this issue could be a reform in education that is well organized and that incorporates AI learning through simulation, corporative

FAQs

Why is trainee readiness considered a critical gap in AI-driven patient safety?

While AI tools improve early diagnostics and monitoring, trainees often have little exposure to these systems and limited confidence in interpreting and acting on alerts. Without preparedness, AI safety gains can be undermined by missed or delayed responses.

What do AI-based early warning systems (AI-EWS) do in hospitals?

AI-EWS detect subtle physiological changes several hours before deterioration becomes clinically obvious. They support recognizing patient deterioration earlier and can reduce unplanned ICU transfers and inpatient deaths.

How can education help close the last-mile problem for trainees using AI tools?

The article calls for teaching AI literacy early, incorporating hands-on simulation, collaboration between clinicians and data scientists, and mandatory digital health modules. It also suggests involving trainees in real design discussions so systems mimic bedside practice.

Sep 29, 2026

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