Jun 2026· 2026 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)· pp. 103-108· 0 citations· 15 references
Abstract
This paper presents the Integrated Predictive Intelligence Tool for Pediatric Health (IPITPH), a mobile-enabled AI-driven decision support system for addressing complex pediatric healthcare challenges in resource-limited, multilingual settings. The system comprises four integrated modules: a hybrid LSTM-DNN predictive analytics module incorporating culturally specific dietary patterns and temporal growth signals for pediatric risk prediction, an LLM-based nutrition optimization module generating personalized meal plans aligned with clinical guidelines and caregiver behavior, a multimodal emergency response module for real-time triage and teleconsultation, and a Retrieval-Augmented Generation-based multilingual conversational AI enabling voice-first caregiver interaction in Sinhala, Tamil, and English. Evaluated on pediatric data from 69 Sri Lankan families supplemented by the PIC clinical database, the predictive module achieved 3.18% MAPE for height prediction and 0.975 AUC for multi-domain risk classification. The nutrition module demonstrated high caloric precision and behavioral adaptation, the emergency module achieved robust triage accuracy with sub-100 ms edge latency, and the conversational module showed significant improvements in clinical accuracy and WHO guideline alignment across all three languages. These results establishing IPITPH as a promising unified AI framework for community-based pediatric healthcare in resource-limited regional contexts, pending broader prospective validation.
CIMAS HealthMate is proposed, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage.
Shamiso Simango, M. Mutandavari· Computer Science and Informa...· 0 citations
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.
Lorenzo Gasparini, Nicola Gobbi, D. Zama et al.· Pediatric Reports· 0 citations
Healthcare digitalization demands platforms capable of integrating heterogeneous biomedical data and delivering AI-powered clinical decision support at scale [1, 2]. The SupportAI project, funded by the Italian National Recovery and Resilience Plan (PNRR) through the Tech4You program (grant ECS 00000009, Spoke 6, Line B, Project 2.1), addresses this challenge through a comprehensive modular platform that unifies real-time clinical data integration, advanced 3D imaging, generative AI assistance, and collaborative tools within a standards-compliant architecture. The platform’s core architectural innovation is the embedding of Retrieval-Augmented Generation (RAG) directly within live HL7 FHIR R4 and DICOM infrastructure. Unlike offline RAG systems that operate on pre-exported or periodically updated datasets, SupportAI queries the Microsoft FHIR Server and Orthanc PACS at inference time, ensuring AI-generated responses always reflect the patient’s current multimodal record with full traceability to FHIR resource IDs and DICOM study UIDs. Additional contributions include Fourier frequency-domain imaging for anomaly detection beyond the spatial domain, an AI-assisted 3D bioprinting pipeline converting DICOM studies into patient-specific STL anatomical models, and a Jitsi-based telecollaboration infrastructure for multidisciplinary remote consultation. The platform comprises seven interconnected microservices: a Multimodal Data Source Module with automated ETL pipelines and GDPR pseudonymization; a FHIR Server organizing clinical information via RESTful APIs; an Orthanc PACS for DICOMweb-compatible image access; an Advanced 3D Imaging Module with GPU-accelerated Cornerstone3D rendering; a 3D Bioprinting Module supporting AI-assisted MONAI segmentation and manual VTK workflows for STL generation; an AI Module implementing LangChain-orchestrated generative assistance grounded in live clinical records; and a Collaboration Module providing Jitsi-based video consultation in virtual 3D laboratory environments. Security is enforced through TLS 1.3, OAuth 2.0/OpenID Connect, and role-based access control. Technical validation at Technology Readiness Level 6 was conducted using Apache JMeter with 10 concurrent virtual users. Results demonstrate platform robustness across all modules. 3D imaging visualization, AI segmentation, and FHIR patient reads achieved sub-15 ms mean response times; the 3D bioprinting pipeline completed end-to-end within minutes even for complex studies; and the generative chat pipeline averaged 23,629 ms, consistent with GPT-4-class deployments under equivalent context complexity. All eight tested configurations achieved a 0.00% error rate under concurrent load. Static code analysis (SonarQube) on the full 1,214k-line codebase achieved Quality Gate: Passed with zero open security issues. Functional correctness was confirmed through complete User Acceptance Testing across all modules and four representative clinical scenarios: 3D imaging and bioprinting, generative AI clinical assistance, AI-assisted image segmentation, and telecollaboration. Physician evaluators reported substantially increased trust in AI outputs due to transparent source attribution, and post-segmentation manual correction requirements were markedly reduced compared to purely manual workflows. These results confirm the technical feasibility of unified multimodal biomedical data integration within a standards-compliant clinical AI platform and establish SupportAI as a solid foundation for AI-powered decision support targeting personalized, accessible healthcare.
Francesco De Rose, Francesco Dattola, Catia Grazioso et al.· ACM International Conference...· 0 citations
Rural communities in India face ongoing challenges in accessing primary healthcare. These challenges include a lack of doctors, geographical isolation, and high consultation costs. This paper discusses an AI-assisted healthcare kiosk, a web-based, fully offline system that provides automated disease prediction, medication recommendations, BMI assessment, cardiovascular risk evaluation, and doctor referrals. It does all this without needing a permanent physician or internet connection. The system uses a Decision Tree classifier along with several machine learning models trained on a structured dataset of 4,920 samples, 132 symptom features, and 41 disease categories. It achieved 100% classification accuracy on the test set. The Flask-based web application features offline speech recognition through Kiosk, text-to-speech output using pyttsx3, TF-IDF vectorisation for natural language processing, and a MySQL backend for managing patient sessions. All ten functional test cases were successful, with an end-to-end response time of about one second. The proposed system proves it is possible to implement AI-powered primary healthcare tools in rural and resource-limited areas.
Thanu Shree M N, Vijayalakshmi M M· International Research Journ...· 0 citations
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
Amit Agrawal, Rashmi Agrawal· The Indian journal of child...· 0 citations
Background: Although pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, identifying individuals who may benefit from PrEP and delivering personalized prevention recommendations remain challenging in routine and digital health settings. Objective: This study aimed to develop and preliminarily evaluate an integrated artificial intelligence framework combining machine learning (ML) for HIV risk stratification and generative artificial intelligence (GenAI) for personalized PrEP recommendation support. Methods: A curated dataset of 2000 de-identified client profiles from Love2Test platform was used for proof-of-concept model development. Profiles were labeled as low or high HIV acquisition risk by domain experts based on structured behavioral information. Multiple ML classifiers were trained and compared using PyCaret. The selected model was integrated with a generative AI model through structured prompting to generate personalized PrEP recommendation content. The integrated framework was evaluated through structured physician assessment by four independent medical doctors. Results: The selected model showed strong internal discrimination for classifying high versus low HIV acquisition risk. The integrated framework also received favorable physician evaluation for clinical accuracy, explanation validity, contextual relevance, and error minimization across fixed and randomly selected profiles. However, because expert labeling was based on structured behavioral indicators closely related to the model inputs, the high internal performance should be interpreted within the context of this proof-of-concept study. Conclusions: The proposed framework provides a structured approach to support HIV risk stratification and personalized PrEP recommendations in a clinician-aligned manner. However, this study was an offline proof-of-concept and did not directly evaluate patient interaction, PrEP uptake, stigma, adherence, or clinical outcomes. Prospective studies using larger and more representative real-world datasets are needed to assess implementation, generalizability, and impact on service engagement and PrEP initiation.
Panyaphon Phiphatkunarnon, Amornphat Kitro, B. Suksatit et al.· Informatics· 0 citations