Search Results (1,732)

Background: Artificial intelligence in medicine has largely relied on supervised training of disease-specific models, limiting scalability in conditions where labeled data are scarce. Large language models (LLMs), which encode broad medical knowledge through large-scale pretraining, offer an alternative paradigm in which structured physiological measurements can be interpreted directly without task-specific model training. Objective: To evaluate whether smartphone-derived ocular motor biomarkers can be translated into clinically meaningful probabilistic assessments of Huntington’s disease (HD) using general-purpose LLMs operating as inference engines. Methods: In this prospective proof-of-concept study, 26 participants (13 with genetically confirmed HD and 13 age-matched controls) completed a standardized ocular motor assessment using a custom smartphone application. Quantitative eye-movement metrics were validated against expert neurologist ratings. Structured physiological features were then provided to four general-purpose LLMs without task-specific training or diagnostic labels, and the models generated an AI-Assigned HD Probability Score (HAIPS). Discriminative performance and associations with clinical severity measures were evaluated. Results: Smartphone-derived ocular motor metrics showed strong agreement with clinician assessments (Spearman ρ = 0.76–0.95; all p < 0.001), confirming preservation of clinically meaningful physiological signals. LLM-derived HAIPS distinguished HD from controls with high accuracy (AUC 0.879–0.944), with no significant differences across models. Discrimination was statistically equivalent to a supervised logistic regression model trained on the same features. HAIPS correlated strongly with established measures of disease severity, including cognitive (MoCA, ρ = −0.86), functional (TFC, ρ = −0.74), and motor impairment (UHDRS, ρ = 0.85) (all p ≤ 0.003). Conclusions: Structured ocular motor biomarkers acquired using a consumer smartphone can be translated into clinically meaningful probabilistic assessments of HD by general-purpose LLMs without disease-specific model training. These findings support a framework in which physiologically grounded digital biomarkers are coupled with general-purpose inference models, potentially enabling scalable assessment in rare neurological diseases where labeled data are limited. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Background: Precise intraoperative localisation of small colorectal tumours during laparoscopic surgery remains challenging due to absent tactile feedback and subserosal tumour location. Current standard methods, particularly India ink tattooing, demonstrate 15–30% failure rates for lesions less than 10 mm, leading to prolonged operative times, incomplete resections, and re-operations. Multiple emerging technologies promise improved localisation, yet comparative evidence remains fragmented. Objective: To map and characterise the current landscape of intraoperative marking and identification technologies for small colorectal tumour localisation during laparoscopic surgery, with emphasis on radiofrequency-based methods and alternative approaches, and to identify evidence gaps guiding future research. Methods: Following PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus databases from January 2000 through December 2025 for studies evaluating tumour localisation technologies in colorectal cancer surgery, including primary tumour localisation during laparoscopic colectomy and localisation of colorectal liver metastases during hepatic surgery, or transferable anatomical applications with documented translational potential to colorectal surgery. Two independent reviewers screened all records, with discrepancies resolved through discussion and a third senior reviewer consulted for unresolved disagreements; data were extracted on technical performance, safety, feasibility, cost-effectiveness, usability, innovation potential, and evidence quality. Results: We included 89 studies comprising 18 colorectal-specific articles and 71 transferable/GI-adjacent studies. Detection success rates ranged from 71% to 100% across modalities. Near-infrared fluorescence with indocyanine green demonstrated the strongest clinical evidence with 75–100% detection across eight colorectal studies encompassing 2134 procedures and seamless workflow integration. Radiofrequency identification systems achieved 91.9–99% detection in feasibility studies with promising tissue penetration of 15–35 mm but limited colorectal validation. Electromagnetic navigation excelled in rigid organs with 85–98% success but showed degraded performance in mobile bowel at 71–75%. Critical evidence gaps included absent head-to-head comparative trials, non-standardised outcome metrics limiting cross-study comparability, and limited long-term safety data with only 14 studies providing follow-up exceeding six months. Conclusions: ICG fluorescence represents the most clinically mature technology identified, representing a priority candidate for colorectal-specific validation in challenging localisation scenarios. RFID systems demonstrate promising characteristics justifying prioritised research investment through adequately powered comparative trials. Future research must emphasise consortium-based comparative effectiveness studies, standardised outcome metrics, and integration with robotic and AI-assisted surgical platforms to accelerate clinical translation. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into implant dentistry, where clinical decision-making depends on the interpretation of complex radiographic and patient-specific data. Although multiple applications have been proposed across diagnostic imaging, treatment planning, intraoperative support and outcome prediction, their clinical validity and real-world applicability remain incompletely defined and their use raises relevant medico-legal considerations. Methods: A narrative review was conducted through a structured search of PubMed/MEDLINE, Scopus, and Web of Science, including English-language studies published between 2010 and February 2026. Clinical and experimental studies, as well as relevant reviews addressing AI applications in implant dentistry, were included. A qualitative thematic synthesis was performed due to methodological heterogeneity. Results: AI applications are mainly concentrated in diagnostic imaging, particularly CBCT analysis, where high levels of performance are consistently reported. In treatment planning, systems support specific decision-making tasks rather than comprehensive strategies, while intraoperative applications are integrated into navigation and robotic systems to improve procedural accuracy. Predictive models for implant outcomes have been developed, although their reliability remains influenced by dataset variability and study design. Conclusions: AI currently represents a supportive tool in implant dentistry, with greater applicability in standardized tasks. Its integration into complex clinical decision-making remains limited, highlighting the need for clinically oriented validation and cautious implementation in practice. Full article

Background/Objectives: Early recurrence after curative-intent resection is a major determinant of poor prognosis in hepatocellular carcinoma (HCC). Artificial intelligence (AI)-driven predictive models have emerged to identify patients at high risk of recurrence but remain incompletely synthesized for early recurrence specifically. This review aimed to identify and appraise AI-driven models predicting early recurrence after surgical resection. Methods: PubMed/MEDLINE, Scopus and Web of Science were searched from inception to November 2025. Eligible studies developed and evaluated AI-driven models predicting early recurrence (≤24 months) after curative-intent hepatectomy as first-line treatment for HCC. Risk of bias and applicability were assessed using PROBAST+AI, and findings were synthesized narratively due to methodological heterogeneity. The review was registered in PROSPERO. Results: Thirty-six studies involving 14,716 patients were included. Most studies originated from China (33/36, 91.7%), were single-center (27/36, 75%), and retrospective (35/36, 97.2%). Magnetic resonance imaging (MRI) was the predominant imaging modality (15/36, 41.7%), followed by computed tomography (CT) (11/36, 30.6%) and ultrasound (US)/contrast-enhanced ultrasound (CEUS) (6/36, 16.7%). Three studies developed non-imaging models, and one combined CT and MRI. In within-study comparisons, multimodal models generally showed better discrimination than unimodal approaches. Peritumoral, habitat-based, and multiphasic strategies appeared promising. However, external validation was reported in only 6/36 studies (16.7%), calibration and decision-curve analysis were inconsistently reported, and most studies had high risk of bias. Conclusions: AI-driven models show potential to predict early recurrence of HCC after curative-intent resection. Nevertheless, evidence remains limited by methodological heterogeneity and restricted geographical diversity, while clinical utility remains inconsistently evaluated, and no model has yet been generalized in clinical practice. Prospective multicenter studies with standardized outcomes, transparent reporting, and external validation are needed for clinical implementation. Full article

Background: Artificial intelligence (AI)-driven markerless motion capture (MMC) technologies are increasingly being integrated into pediatric healthcare to improve the assessment and management of movement disorders. These video-based systems enable non-invasive motion analysis without wearable sensors, facilitating more natural movement assessment in children, particularly those with neurological or developmental conditions. Objectives: We evaluated the clinical applicability of AI-based MMC tools in pediatric settings for diagnosis, monitoring of motor development, and rehabilitation. Methods: This systematic review was registered in PROSPERO (CRD42024511787) and conducted by two independent reviewers, with a third reviewer resolving disagreements. The literature published between 2018 and 2025 was systematically searched. Studies involving pediatric populations or clinically relevant pediatric applications of MMC were included. Results: Of 1521 identified studies, 52 were finally selected. The included studies evaluated populations across a wide age range. However, seven of the included articles were specifically focused on underage populations. Infant studies primarily analyzed whole-body movements, emphasizing the relevance of global motor patterns in early development. OpenPose and AlphaPose were the most frequently used frameworks in pediatric research because of their automatic full-body key point detection, whereas DeepLabCut was commonly selected for its customizable labeling capabilities. Theia3D emerged as a promising clinically applicable solution with high accuracy. Most studies evaluated kinematic parameters as objective markers of motor performance and development. However, methodological heterogeneity and limited pediatric-specific validation remain important limitations. Conclusions: AI-driven MMC technologies show considerable potential to support objective, accessible, and child-friendly movement assessment in pediatric clinical practice. Full article

The rapid integration of artificial intelligence (AI) into mental healthcare presents opportunities and ethical challenges, particularly for complex conditions like obsessive–compulsive disorder (OCD). In this perspective, we argue for a Dual Imperative: establishing safety architectures for AI-powered therapeutic tools to prevent algorithmic sycophancy (symptom accommodation), while mandating explainable AI (XAI) in prognostic models to ensure clinical auditability. In therapeutics, we propose a Guardian Angel architecture that utilizes patient-specific fear hierarchies and linguistic stance detection to distinguish compulsive reassurance-seeking from legitimate patient questions. This approach transforms potential therapeutic ruptures into opportunities for distress tolerance via the Digital Ulysses Pact, a patient-authorized, algorithmically enforced response prevention protocol. In diagnostics, we address the black box problem in precision psychiatry. We argue that as AI evolves from detection to high-stakes treatment selection, safety and accountability become a prerequisite for clinical application. Although distinct in implementation, these architectures form an integrated framework for aligning therapeutic and diagnostic AI. These architectures are not parallel tracks but a unified ecosystem: A patient’s XAI-audited profile can inform the Guardian Angel’s configuration, while the longitudinal data gathered during therapy enriches diagnostic precision. Grounded in ethical principles and best practices in OCD, this suggests a path toward AI that is auditable in its diagnostic logic, firm in its therapeutic boundaries, and enforceable through emerging regulatory frameworks. Full article

Traditional evaluations of artificial intelligence (AI) systems in the dynamic, operator-dependent, and time-sensitive field of interventional radiology (IR), focusing solely on algorithmic performance, often fail to capture their real-world clinical impact. This narrative review aims to provide an overview of the current state of the art of AI integration in IR through human–AI interaction (HAI), while offering a critical perspective on their clinical integration, limitations, and future directions. A comprehensive survey of recent literature was performed, focusing on AI applications across procedural phases. The review emphasizes systems providing decision support, real-time procedural verification, and immersive interfaces (augmented and virtual reality), while critically evaluating determinants of effective clinical adoption. AI has shown preliminary potential to support operator performance in selected interventional radiology tasks, although most applications remain experimental, retrospective, or evaluated in phantom or preclinical settings. Potential benefits include structuring uncertainty in patient selection and procedural planning, supporting assessment of device positioning and treatment outcomes, and integrating AI-derived outputs into the operator’s spatial field through immersive technologies. The clinical utility of these systems appears to be influenced by human–AI interaction, with interpretability, workflow integration, and trust calibration representing key determinants of effective use beyond algorithmic accuracy alone. The potential value of AI in interventional radiology appears to derive from its integration into human decision-making rather than from standalone predictive performance alone. A human-centered, interaction-based model supports understanding current applications, address challenges, and guide the development of adaptive, real-time systems for dynamic procedural environments. Full article

Artificial intelligence (AI) is increasingly influencing nanopharmaceutical development by supporting the transition from empirical formulation screening toward predictive, data-driven, and translationally oriented design. Nanocarrier-based therapeutics are governed by nonlinear relationships among material composition, physicochemical attributes, manufacturing parameters, biological identity, pharmacokinetics, toxicity, and therapeutic performance. In this review, we examine how AI can contribute to nanopharmaceutical development from predictive formulation design to clinical translation. We synthesize current applications of machine learning, deep learning, physics-informed modeling, hybrid mechanistic–AI approaches, and automated optimization workflows, with emphasis on critical quality attribute modeling, multi-objective optimization, design of experiments, quality-by-design, process analytical technology, digital twins, and continuous manufacturing. We also discuss applications involving nano–bio interactions, pharmacokinetics, toxicity, immunogenicity, and precision nanomedicine. AI-based approaches can support rational nanocarrier design, identify nonlinear formulation–property relationships, guide optimization, improve process understanding, and integrate heterogeneous experimental, biological, and manufacturing datasets across diverse nanopharmaceutical platforms. These methods are particularly relevant for modeling protein corona formation, cellular uptake, intracellular trafficking, biodistribution, pharmacokinetics, toxicity, immunogenicity, and patient-specific responses. However, translational implementation remains limited by fragmented datasets, inconsistent reporting standards, limited interpretability, insufficient external validation, uncertain predictions, poorly defined applicability domains, and evolving regulatory expectations for adaptive computational models. Overall, AI should be viewed not only as an optimization tool, but also as a translational framework connecting formulation science, biological prediction, manufacturing control, and clinical implementation. Future progress will depend on standardized data infrastructures, explainable and externally validated models, uncertainty quantification, applicability-domain definition, hybrid mechanistic–AI frameworks, regulatory-ready documentation, and clinically relevant case studies. Full article

Background/Objectives: Periodontitis is a highly prevalent chronic disease requiring accurate diagnosis for effective treatment planning. Artificial intelligence (AI) has emerged as a potential tool to support clinical decision-making. This study aimed to compare the diagnostic performance and classification error patterns of a large language model (LLM) and a neural network (NN) in periodontitis classification according to the current staging and grading system. Methods: This retrospective study included 110 patients with periodontal disease. Clinical and demographic variables (age, sex, smoking status, number of teeth, API, BOP, PPD, and CAL) were analyzed. Reference diagnoses were established by two experts. Cases were evaluated using an LLM and a neural network. Model performance was assessed using accuracy, confusion matrices, and Cohen’s kappa coefficient, along with error analysis. Results: The LLM achieved 62% accuracy for stage and 63% for grade classification (κ = 0.48). The neural network showed higher performance, with 85% accuracy for stage and 79% for grade (κ = 0.79 and κ = 0.67, respectively). The LLM more often underestimated disease severity, whereas the neural network tended to overestimate progression. Differences between models were statistically significant (p < 0.0001). Conclusions: In this dataset and classification task, the task-specific neural network demonstrated higher diagnostic performance than the evaluated large language model. However, the findings should be interpreted in light of the fundamentally different training paradigms and intended applications of these AI systems. Further research is required to optimize and validate AI-based approaches for clinical use. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, biological heterogeneity, and persistent challenges in staging and treatment selection. This narrative review summarizes current and emerging applications of AI across lung cancer screening and early detection, imaging-based staging and prognostication, tissue and liquid biopsy-based tumor characterization, treatment planning, surgical and intraoperative guidance, and drug discovery. In imaging, deep learning models have demonstrated high performance in pulmonary nodule detection, risk stratification, and prediction of molecular alterations, while also showing promise in improving screening efficiency and reducing interpretive variability. In pathology and liquid biopsy domains, AI enables prediction of driver mutations, immunotherapy response, and survival outcomes directly from histopathology slides, circulating tumor DNA, and other blood-based biomarkers, facilitating minimally invasive precision oncology approaches. In treatment planning and delivery, AI systems are being developed to support clinical decision-making, surgical planning (through advanced image segmentation and delineation of operative anatomy), and intraoperative navigation through robotic and computer vision-enabled platforms. Despite these advances, significant barriers remain, including limited real-world validation, algorithmic biases, workflow integration issues, and unresolved ethical and legal concerns. Future progress will depend on the development of transparent, clinically validated, and generalizable AI systems that augment rather than replace the expertise of clinical providers and healthcare teams. Active engagement from pulmonologists, oncologists, radiologists, and thoracic surgeons will be essential in guiding safe implementation and ensuring that AI-driven innovations translate into meaningful improvements in patient outcomes. Full article

Background: Cardiac point-of-care ultrasound (POCUS) is widely used in emergency and acute care settings. Still, broader use remains limited by operator dependence and variability in image acquisition and interpretation. Artificial intelligence (AI), including machine learning and deep learning methods, has been applied to cardiac POCUS to support image acquisition, automate quantitative measurements, and assist interpretation. Methods: We performed a narrative review of current applications of AI-assisted cardiac POCUS. A targeted literature search of PubMed and Google Scholar from 2018 to 2026 was conducted using terms related to AI, machine learning, deep learning, and cardiac ultrasound. Studies evaluating AI-assisted cardiac ultrasound in clinical, educational, or image-acquisition settings were included, with emphasis on recent, clinically relevant applications. Results: The most developed application of AI-assisted cardiac POCUS is an automated assessment of left ventricular systolic function, particularly the left ventricular ejection fraction (LVEF), where multiple studies report agreement with expert interpretation or formal echocardiography and improved performance among novice users. AI-assisted tools have also been evaluated for pericardial effusion detection, guidance for image acquisition, and education. More complex applications, including diastolic function assessment and hemodynamic measurements such as LVOT-VTI, remain less well validated and more dependent on image quality. Across studies, performance is closely linked to image acquisition quality and has often been evaluated under controlled rather than real-world conditions. Conclusions: Current evidence supports AI-assisted cardiac POCUS primarily as a decision-support tool, with the strongest data for automated assessment of LVEF. Other applications remain investigational. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article