Jul 2026· The Indian journal of child health· Vol 13, pp. 72-83· 0 citations
TL;DR
This narrative review summarizes current evidence regarding AI applications in ambulatory pediatrics, preventive pediatrics, and pediatric outpatient care, highlighting their clinical utility, limitations, implementation challenges, and future directions.
Abstract
Artificial intelligence (AI) is increasingly being incorporated into ambulatory and preventive pediatric practice through advances in machine learning, deep learning, computer vision, natural language processing, and mobile health technologies. The growing availability of electronic health records, wearable devices, telemedicine platforms, and digital health applications has created new opportunities for AI-assisted screening, risk prediction, preventive care, and outpatient clinical decision support. In pediatric healthcare, AI has emerging applications in developmental and autism screening, vision and hearing assessment, growth and nutrition monitoring, vaccination programs, infectious disease surveillance, symptom triage, prescribing support, remote patient monitoring, and parent-facing digital health tools. These technologies have the potential to improve early diagnosis, enhance preventive interventions, expand access to care, and support more personalized management of pediatric patients in outpatient settings. However, significant challenges remain, including limited pediatric-specific validation, algorithmic bias, data privacy concerns, regulatory uncertainties, workflow integration barriers, and the need to maintain trust among clinicians and caregivers. This narrative review summarizes current evidence regarding AI applications in ambulatory pediatrics, preventive pediatrics, and pediatric outpatient care, highlighting their clinical utility, limitations, implementation challenges, and future directions. This article is the second in a four-part series on AI in pediatrics; subsequent articles will discuss AI applications in pediatric inpatient and critical care settings, as well as the ethical, regulatory, and future implications of AI in child healthcare
It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.
Vinit Kumar Ramawat, G.PRABHAKARAN, Pinki Das et al.· International journal of com...· 0 citations
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.
Unknown authors· Journal of Cognitive Human-C...· 0 citations
Artificial intelligence (AI) is rapidly transforming healthcare, with growing impact on maternal and child health (MCH) through advances in machine learning, deep learning, computer vision, generative models, and conversational systems. This article provides a comprehensive synthesis of current AI applications in MCH, structured across six key domains: predictive modeling, image analysis, deep learning and interpretability, generative and multi-omics approaches, conversational AI, and environmental and lifestyle analytics. Drawing on recent literature and the translational experience of the Spanish RICORS-SAMID network, we analyze how these technologies are being integrated into clinical, preventive, and assistive workflows. Across domains, AI demonstrates strong potential for early risk prediction (e.g., preeclampsia, fetal growth restriction, neonatal outcomes), automated image interpretation, biomarker discovery, and personalized decision support. However, despite promising performance metrics, most systems remain at the proof-of-concept stage, with limited external validation, scarce prospective evaluation, and incomplete integration into real-world clinical pathways. Key translational gaps include data heterogeneity, lack of interoperability, insufficient explainability, and challenges related to bias, fairness, and regulatory compliance. We argue that the next phase of AI in MCH must shift from static prediction toward longitudinal, mechanism-aware, and clinically actionable systems, supported by robust validation and multidisciplinary collaboration. Particular emphasis is placed on equity, as the benefits of AI must extend to low-resource settings where maternal and neonatal morbidity remains highest. By bridging technical innovation with clinical implementation, coordinated research networks such as RICORS can play a critical role in accelerating the safe, effective, and equitable deployment of AI in maternal and child healthcare.
Paula Domínguez del Olmo, Juan D Arévalo, C. Villalaín et al.· Women's Health· 0 citations
Artificial intelligence (AI) is increasingly being applied in healthcare, with growing relevance to clinical nutrition. This narrative review examines current and emerging uses of AI in nutrition care within the Nutrition Care Process framework, with attention to assessment, monitoring and evaluation, diagnosis, intervention, and clinical support tools. Current applications include AI-assisted dietary assessment using image recognition, wearable sensors, analysis of continuous glucose and other physiologic data for early risk detection, and support for malnutrition screening and diagnosis. AI is also being explored for identifying micronutrient deficiencies and complications of nutrient excess, as well as for screening and early intervention in eating disorders. In nutrition intervention, AI has potential to support personalized dietary planning, nutrition support in intensive care settings, behavioral interventions, and precision nutrition approaches such as digital twins. Additional applications include clinical decision support and documentation assistance. However, despite its usefulness, concerns about AI systems exist. Its performance depends on the quality of the data used to train it; it can introduce bias, and it can produce inaccurate or misleading outputs. In addition, overreliance on AI may also reduce clinician attentiveness and contribute to cognitive errors. For these reasons, AI should be regarded as a support tool rather than a replacement for human clinical care. Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.
K. Mauldin, Anthony D. Pham, Sneha Dodaballapur et al.· Nutrients· 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
Artificial Intelligence (AI) is rapidly transforming healthcare by supporting clinical decision-making, patient monitoring, documentation, education, research, and personalized care. Mental health nursing is an important area in which AI has the potential to improve early identification of mental health problems, continuous monitoring, therapeutic support, risk assessment, and access to care. Recent advances in machine learning, natural language processing, predictive analytics, conversational agents, and generative AI have expanded the possibilities for supporting individuals experiencing depression, anxiety, psychosis, substance-use disorders, and other mental health conditions. Evidence suggests that AI-based systems can assist in detecting symptoms, predicting clinical risks, monitoring changes in behaviour and mood, and providing accessible digital interventions. However, the use of AI in mental health also creates significant ethical and professional concerns, particularly regarding privacy, confidentiality, informed consent, algorithmic bias, transparency, accountability, patient safety, therapeutic relationships, and the risk of over-reliance on automated systems. Mental health nurses are uniquely positioned to ensure that AI remains person-centred and clinically appropriate because they combine continuous patient observation with therapeutic communication and holistic assessment. This article reviews the major applications and opportunities of AI in mental health nursing, discusses ethical and professional challenges, and proposes future directions for education, research, clinical practice, and policy. AI should be regarded as a supportive technology rather than a replacement for professional nursing judgment or human therapeutic relationships.
Payal Sharma, Milan Agravat, Pranali Mackwan et al.· Adolescência e Saúde· 0 citations
