Toward Child-Centred Artificial Intelligence in Pediatric Emergency Medicine: A Perspective on Clinical Decision Support, Stakeholder Engagement and Education
This perspective aims to evaluate the integration of AI and machine learning into pediatric emergency departments (PEDs) across three core domains: clinical decision support, stakeholder engagement, and medical education.
Abstract
Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing evidence supporting its applicability across time-critical clinical environments. This perspective aims to evaluate the integration of AI and machine learning (ML) into pediatric emergency departments (PEDs) across three core domains: clinical decision support, stakeholder engagement, and medical education. Within clinical decision support, ML architectures have demonstrated high predictive performance across several high-acuity clinical scenarios, including triage stratification, pediatric traumatic brain injury risk classification, early sepsis detection and clinical deterioration prediction, and dermatological assessment. Model interpretability and real-world implementability remain critical prerequisites for clinical adoption, with explainability methods representing fundamental instruments to enhance transparency and stakeholder trust. Regarding stakeholder engagement, the triadic dynamic among clinicians, caregivers, and patients defines a unique communication challenge in PEDs, with large language models (LLMs) showing preliminary utility; however, stakeholder-inclusive model validation and robust data privacy protections for minors remain key challenges, particularly regarding legal ambiguities of LLM deployment in clinical pipelines. In medical education, AI-driven simulation platforms and LLM-generated adaptive curricula represent promising tools for competency-based training across pediatric emergency scenarios. Future directions emphasize the imperative of prospective multicenter validation in pediatric-specific cohorts, rigorous data quality standards addressing conformance, completeness, and plausibility, and the development of pediatric-tailored governance frameworks. Real-world implementation will require the systematic involvement of all stakeholders—including children, caregivers, clinicians, developers, and institutions—as co-designers of equitable, transparent, and safe AI systems for this uniquely vulnerable population.
Background: Acute care environments—emergency departments (EDs) and intensive care units (ICUs)—demand rapid, precise clinical decisions under extreme time pressure and uncertainty. Artificial intelligence (AI) offers transformative potential across multiple healthcare disciplines by providing real-time data synthesis, advanced pattern recognition, and personalized decision support. Aim: This narrative review synthesizes evidence from 2015–2025 on AI applications across the acute care continuum, examining multidisciplinary implications involving emergency medicine, radiology, laboratory medicine, general practice, nursing, pharmacy, and physical therapy. Methods: A systematic literature search identified 40 peer-reviewed articles, systematic reviews, and consensus statements for narrative synthesis. Results: AI enhances triage accuracy through machine learning algorithms, improves diagnostic precision in imaging via deep learning, enables early detection of sepsis and deterioration through predictive analytics, optimizes referral decisions in primary care, supports nursing surveillance and patient monitoring, enhances medication safety through clinical decision support systems, and enables personalized rehabilitation planning. Predictive models demonstrate AUC-ROC values of 0.75–0.95 across outcomes, with sepsis mortality reductions of up to 20% and medication accuracy improvements of 30%. However, fewer than 2% of AI models achieve clinical deployment, with barriers including interoperability gaps, poor model generalizability, algorithmic bias, and limited clinician trust. Conclusion: AI holds substantial promise for acute patient management across all healthcare disciplines, but realizing its potential requires standardized validation, explainable models, workflow integration, and comprehensive interprofessional clinician education.
Khalid Ali Al-Shehri, Adel Mohammed Asiri, Abdullah Saeed Al- ALMAZARIQAH et al.· Journal of medical and life...· 0 citations
Background: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, significantly influencing clinical decision-making processes. AI-driven systems, including machine learning algorithms, deep learning models, natural language processing, and clinical decision support systems, have demonstrated the ability to analyze vast amounts of healthcare data, identify patterns, predict outcomes, and support evidence-based clinical decisions. As healthcare systems face increasing patient complexity, workforce shortages, and demands for improved quality of care, AI offers opportunities to enhance diagnostic accuracy, treatment planning, risk prediction, and healthcare efficiency. However, concerns regarding algorithm transparency, ethical accountability, data privacy, bias, and professional acceptance continue to challenge its widespread adoption.
Objectives
To systematically review the evidence regarding the role of artificial intelligence in clinical decision-making across healthcare settings.
To identify the benefits and effectiveness of AI-supported clinical decision-making systems in improving patient outcomes.
To examine challenges, ethical considerations, and barriers associated with AI implementation in clinical decision support.
To evaluate implications for healthcare professionals, particularly nurses and physicians, in AI-assisted clinical practice.
Methods: This systematic review was conducted according to Joanna Briggs Institute (JBI) methodology and reported following PRISMA 2020 guidelines. Electronic databases including PubMed, Scopus, Web of Science, CINAHL, ScienceDirect, and Google Scholar were searched for peer-reviewed studies published between 2019 and 2025. The review question was structured using the PICOT framework. Methodological quality was assessed using appropriate JBI Critical Appraisal Tools. Due to heterogeneity among study designs and outcomes, findings were synthesized narratively.
Results: Thirty-five studies met the inclusion criteria. Evidence demonstrated that AI-assisted clinical decision-making significantly improved diagnostic accuracy, risk stratification, treatment planning, predictive analytics, medication management, and workflow efficiency. AI systems were particularly effective in radiology, oncology, cardiology, intensive care, and chronic disease management. However, challenges related to explainability, algorithmic bias, legal liability, data privacy, workforce adaptation, and ethical governance were consistently reported.
Conclusion: Artificial intelligence has substantial potential to strengthen clinical decision-making and improve healthcare outcomes. Successful integration requires robust regulatory frameworks, transparent algorithms, ethical governance, clinician training, and preservation of patient-centered care principles. AI should function as a supportive tool that enhances rather than replaces human clinical judgment.
Arun James, Dr. Jomon Thomas, Dr. Deepika Verma et al.· PAIN, JOINTS, SPINE· 0 citations
AI-integrated Clinical Decision Support Systems (AI-CDSS) consolidate patient data with medical expertise to aid healthcare practitioners in deciding their approach. They promote guideline compliance, enhance treatment safety, and lead to more accurate diagnoses. This narrative review examines the potential role and emerging applications of AI-CDSS in strengthening primary healthcare systems by reviewing existing literature and evidence to evaluate current implementations, benefits, and limitations of AI-CDSS in the local healthcare context, with a particular focus on gaps to be addressed in the Pakistani healthcare landscape. Currently, although Pakistan is only utilizing AI-CDSS at a small scale and limited to tertiary care settings, these efforts encompass multiple domains such as drug safety, diagnostic help, and treatment planning. Findings have shown positive results and advocate for implementing AI-CDSS systems on a larger scale, which can reduce the burden on the healthcare system in Pakistan. Unfortunately, multiple concerns, such as user adoption and warning fatigue, must be dealt with before implementing a large-scale application of this system. Our research proposes a framework for the sustainable integration of AI-CDSS into Pakistan's healthcare system, with a focus on scaling solutions to primary care environments such as Basic Health Units and rural health centers. Overall, AI-CDSS holds significant potential to enhance accessibility, quality, and capacity of primary healthcare in Pakistan, provided that existing systemic and operational barriers are effectively addressed.
Huma Ahsan, F. Qazi, Amna Amir Jalal et al.· Liaquat National Journal of...· 0 citations
The integration of generative artificial intelligence (AI) and large language models (LLMs) into healthcare has accelerated dramatically, with emergency medicine emerging as a particularly dynamic yet challenging domain for clinical deployment. This comprehensive narrative review synthesizes contemporary literature to examine current clinical applications, critical safety considerations, implementation scalability and governance, and future research priorities surrounding generative AI in acute care settings. We examine the current clinical applications, critical safety considerations, implementation scalability and governance, and future research priorities for generative AI in acute care settings. Current emergency medicine applications span automated documentation via ambient clinical intelligence systems, clinical decision support for triage and diagnostic prediction, patient communication tools including discharge summary generation, and multilingual data extraction supporting care transitions. Despite promising efficiency gains - including documented reductions in clinician burnout from 50.6% to 29.4% and modeled documentation time savings of up to 7.1 hours per shift cycle - substantial safety concerns persist, with hallucination rates ranging from 26% to 36% across automated pipelines and systematic misclassification in high-acuity triage tasks. We address automation bias (26% increased risk) and data privacy and governance risks, and identify algorithmic equity as a critical research priority. We conclude that while generative AI holds transformative potential to reduce clerical burden and augment clinical reasoning, its successful deployment in emergency medicine requires rigorous attention to clinical safety, health equity, workflow integration, and human‑factors considerations.
Klaudia Kwolek, Wiktoria Laskowska, M. Pilarek et al.· International Journal of Inn...· 0 citations
Abstract Artificial intelligence (AI) is increasingly embedded across oncology workflows, with applications spanning initial assessment, diagnostic work-up, treatment selection, longitudinal monitoring, and survivorship care. A clinically oriented synthesis of these use cases is needed to guide real-world adoption and highlight implementation challenges. A narrative review of recent clinical, translational, and health-services literature was conducted, focusing on AI tools deployed in routine or near-term oncology practice. The manuscript organizes evidence along the cancer care continuum, from first presentation to outcome prediction, and integrates ethical, legal, and regulatory perspectives. AI systems now support ambient documentation and natural language processing (NLP) for initial consultations, advanced imaging and digital pathology for diagnosis, and multimodal decision support for systemic therapy, radiation, and surgery. Additional applications include toxicity and adverse-event prediction, real-time symptom and liquid biopsy monitoring, risk-adapted follow-up, and survivorship risk stratification, although prospective validation and interoperability remain uneven. AI has moved from proof-of-concept to a practical adjunct to oncology decision-making, with demonstrable potential to enhance precision, efficiency, and patient experience across the cancer continuum. Realizing this promise will require validated, explainable, and equitable systems, robust data governance, and deliberate design of human–AI collaboration within everyday oncology practice. AI would not be a replacement for human expertise, but a pivotal tool to aid cancer patient care.
G. Divekar, Bharat Bhosale· Indian Journal of Medical an...· 0 citations
Artificial intelligence (AI) is increasingly influencing perioperative care by enhancing decision support, resource management, documentation, image analysis and patient safety. This article presents a narrative conceptual review of four commonly encountered AI model types: random forests, support vector machines, convolutional neural networks and large language models. It explores their clinical applications, interpretability, limitations and relevance to perioperative and surgical settings. Particular emphasis is placed on model explainability, evaluation metrics such as accuracy, F1 score and area under the receiver operating characteristic curve, hallucination risk and the challenges associated with opaque ‘black box’ systems, including non-interpretable algorithmic frameworks. The discussion is contextualised within the UK’s regulatory and governance frameworks, including the National Health Service Digital clinical safety standards, Medicine and Healthcare products Regulatory Agency requirements, National Institute for Health and Care Excellence evaluation tiers and data protection under UK General Data Protection Regulation. By improving clinician understanding of how AI systems function, fail and should be governed, this article aims to support informed, safe and ethical adoption of AI technologies in perioperative environments.
Kevin Patrick Cairns· BMJ Innovations· 0 citations