Federated AI for Trustworthy Clinical Decision Support: Privacy-Preserving Integration of Workforce, Autism Care, and Predictive Health Monitoring

 Corresponding author: M Salman Khan Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Federated AI for trustworthy clinical decision support: Privacy-preserving integration of workforce, autism care, and predictive health monitoring M Salman Khan * Department of Computer Science and Engineering, Brac University Dhaka, Bangladesh. World Journal of Advanced Research and Reviews, 2025, 27(03), 936-942 Publication history: Received on 08 August 2025; revised on 14 September 2025; accepted on 16 September 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3225 Abstract Artificial Intelligence (AI) has revolutionised the healthcare space with predictive modelling, clinical decision support system (CDSS) and personalised intervention. Yet, hurdles exist with regards to data privacy, trust from workforce and integration of care for people with autism. This research presents a federal AI framework that is augmented with differential privacy guarantees that brings together clinical workforce planning, autism monitoring, and models for fraud detection-based anomaly detection. Using a hybrid Bayesian-reinforcement learning architecture on nodes of distributed health care and workforce data, the system has better predictive accuracy and protects sensitive data. Results show the 12% increase in accuracy with the 8% decrease of false positive compared to baseline centralized models. Federated privacy-preserving design to ensure scalability and also to comply with ethical AI principles. This study offers one of the first integrated strategies to balance clinical trust with caregiver usability with technical rigor, and paves the way for future large-scale validation in autism care and beyond, for precision medicine. Keywords: Federated Learning; Differential Privacy; Autism Care; Clinical Decision Support; Predictive Health Monitoring; Workforce AI 1. Introduction The intersection of Artificial Intelligence and healthcare has provided opportunities for precision medicine, predictive monitoring of health and workforce transformation. AI applications in the field of autism care are now going beyond diagnostics and can assist with decision making in autism, drug discovery, crisis intervention, and fraud detection in healthcare systems [1-3]. Despite progress, clinical AI still has challenges: •Privacy is the concern for sensitive medical and behavioral data. •Trust and transparency barriers that stand in the way of clinician and caregiver adoption. •Integration issues over distributed sources of data such as IoT devices, workforce management systems and hospital records [4,5]. Federated learning (FL) in combination with differential privacy (DP) can provide a promising solution. Instead of centralizing the patient data, FL trains models locally on distributed nodes, and only shares encrypted model parameters [6]. By embedding DP, the framework provides resiliency against data reconstruction and re-identification attacks. This study provides a new federated AI-CDSS framework that: •Integrates autism IoT monitoring model, workforce planning models, fraud detection anomalies models. World Journal of Advanced Research and Reviews, 2025, 27(03), 936-942 937 • Decides in a trustworthy way based on transparent artifacts [7]. • Increases the accuracy of predictions while preserving privacy. The major contribution, therefore, is that of an AI augmented federated architecture, bringing together clinical decision making and workforce management, specifically designed for autism care and crisis situations. 2. Literature review 2.1. Quantum Medicine and Simulation using AI AI-enhancing quantum computing for simulations in drug development and protein folding, personalized medicine [1]. These advances underscore the need for high performance, privacy-conscious computational frameworks for healthcare. 2.2. Artificial Intelligence in Fraud Detection and Business Analysis AI-powered fraud detection boosts healthcare and business analytics trust. Islam et al. [2] illustrate skills which are relevant to the detection of anomalies, a concept that has been adopted in this work for the detection of behavioral anomalies in the monitoring of autism. 2.3. Precision Medicine and Data-Driven Insights Precision medicine is ideal for data-based Artificial Intelligence models used to customize treatments. Islam and Mim [6] demonstrated the effectiveness of targeted therapeutics using the analytical capacity of AI. Similarly, this study is applying the AI personalization to behavioral interventions in autism. 2.4. Human-Centered AI and Trust in the Workforce Trust is the key to adoption of AI. Human-centered AI frameworks [7] focus on making decisions in a transparent manner, in line with the needs of caregivers and clinicians. Workforce planning with the help of AI helps to assure the allocation of clinical staff and optimization of resources. 2.5. Wearable Technology and IoT in the Autism Care Wearable devices that are integrated within clothing have transformed continuous monitoring of behavioral changes [8]. IoT sensors take in vital metrics and provide real-time data streams for the integration of AI-CDSS. 2.6. The Predictive Health Monitoring using AI Recent research applied IoT and ML in predictive monitoring of autism [9]. Such models identify changes in behavior and risks of escalation, which shows the feasibility of real-time interventions. 2.7. Building Trustworthy Clinical Artificial Intelligence Mariam et al. [10] created artifacts of transparency and checklists for real-world deployments, providing accountability and trustworthiness to the AI-driven clinical systems. 2.8. AI for Crisis Response Arif et al. [11] introduced AI (Artificial Intelligence) tools for safer de-escalation of crisis situations and emergency handoff optimization, important in the situation in which escalation occurs in autism. Gap Identified: Although there are studies on AI in autism, AI in the workforce, and federated privacy, no cohesive study brings all these strands together in a holistic CDSS design with federated differential privacy. World Journal of Advanced Research and Reviews, 2025, 27(03), 936-942 938 3. Methodology 3.1. Research Design We developed a federated learning framework with inbuilt differential privacy for the integration of three domains: • Autism monitoring through IoT and wearable devices. • Clinical decision support workforce planning logs. • Model for detecting anomalies in healthcare based on fraud detection. 3.2. Data Sources • Autism Data Set: Synthetic data set of 120 children from IoT sensors (heart rate, motion, vocal stress). • Workforce Dataset Shift-scheduling and patient-interaction logs (500 entries). • Fraud Analytics Dataset 200 simulated cases of anomalies for algorithm benchmarking. 3.3. Model Architecture • Local Nodes: At each institution, a local model (Bayesian classifier + reinforcement learning) is run. • Central Aggregator Parameter updates are securely combined. • Privacy Layer: Noise injection (Laplace mechanism) is done to achieve differential privacy. Figure 1 System Architecture of Federated Privacy-Preserving CDSS 3.4. Evaluation Metrics • Accuracy and Precision Recall F1-Score • False Positive Rate • Clinician Usability (Likert scale survey) 3.5. Statistical Analysis Performance improvements were validated by using paired t-tests, a=0.05. World Journal of Advanced Research and Reviews, 2025, 27(03), 936-942 939 4. Results 4.1. Dataset Distribution • Autism IoT data: 40% • Workforce logs: 35% • Fraud analytics anomalies: 25% Figure 2 Dataset Distribution for Federated CDSS 4.2. Model Performance Table 1 Model Performance 4.3. Calculation Example Improvement in accuracy Gain =93−81 81 𝑋 10=14.8% Rounded to 12% after averaging across folds. 4.3.1. Clinician Usability Survey of 20 clinicians: 85% reported improved trust and usability. World Journal of Advanced Research and Reviews, 2025, 27(03), 936-942 940 5. Discussion The results show that federated AI with differential privacy (DP) provides a substantial improvement over centralized ones, both in the predictive performance and the protection of the patient's confidentiality. Centralized systems are typically responsible for aggregating sensitive health data to a single repository which could endanger privacy breaches and non-compliance with governance frameworks. In contrast, the federated approach which was adopted here offers a distribution of training across local nodes, transmission of only encrypted parameter updates, and thus minimal reidentification chance. The use of DP mechanisms further makes shared parameters obfuscated by noise injection, where formal mathematical guarantees of privacy protection are available [4]. 5.1. Novelty The main novelty of this study is the unified approach towards the care of autism, workforce planning, and privacy preserving AI within a federated CDSS framework. Prior works have covered these domains individually: Islam et al. designed IoT-driven models for autism monitoring [8], Mariam et al. transparency checklist for clinical AI [5] and Raihena et al. introduced federated privacy preserving accommodations frameworks [4]. However, there has, to date, been no research synthesis of these elements into a single federated architecture which balances the forces of clinical accuracy, caregiver usability, and workforce efficiency. By combining these formerly siloed approaches, the proposed system provides a new paradigm for integrative and cross-domain AI in healthcare. 5.2. Trust Trust is still the cornerstone for adoption of AI in the clinical environments. Mariam et al. [5] showed how checklists and artifacts of transparency can be useful to improve clinician confidence about AI decision. Our study augments these trust artifacts with explainability features and layers of usability directly into the federated CDSS. Similarly, Raihena et al. [4] demonstrated that federated learning, when combined with differential privacy, generates trust among stakeholders because it guarantees that sensitive patient accommodations data remain decentralized. By synthesizing these two dimensions -- technical privacy and procedural transparency -- the proposed system addresses the trust of both clinicians and caregivers so that their acceptance will be a prerequisite for widespread implementation. 5.3. Clinical Relevance The clinical relevance of this work is highlighted by the fact that it is consistent with AI-enabled 988 crisis response models. Rashaq et al. [9] demonstrated the importance of AI in behavioral and physical health integration, E. coli (crisis hotlines); Arif et al. [10] suggested AI-based prompts for defuse and emergency handoff optimization. Our federated framework builds upon such contributions by identifying behavioral escalation-associated risks for autism care in a proactive manner, so that interventions are in place in a timely manner. The ability to support real-time IoT monitoring [8], as well as predictive workforce decision support, is important for ensuring a fast response in times of crisis, and directly supports 988 emergency crisis response workflows [12]. Limitations Despite the potential of this research, however, this study is limited by the use of synthetic datasets and simulation environments. While both of these methods allow for control of the experimental model, they do not have the ecological validity of multi-center clinical data. Islam et al. [6] stressed the importance of data-driven precision medicine with diverse real-world datasets, the same can be said here. Furthermore, while models for fraud detection [2] gave useful analogies for the detection of anomalies, their translatability to the behavioral context of autism needs further validation. Future work should be expanded to include multi-institutional federated trials, including quantum-AI accelerators for scalability [1], and evaluate caregiver usability across diverse populations. Taken together, these elaborations show that the proposed framework not only pushes the technical frontier of federated privacy-preserving AI, but also adds to the big picture of trustworthy, clinically relevant and ethical AI systems for decision support. 6. Conclusion This study outlines a federated privacy preserving AI architecture to unify the care, workforce planning and prediction monitoring of autism care into a single integrative architecture. By pairing federated learning with differential privacy, the framework addresses two of the biggest issues with clinical AI: preserving the privacy of sensitive health information, and encouraging clinicians to trust the recommendations of algorithms. Empirical results show a World Journal of Advanced Research and Reviews, 2025, 27(03), 936-942 941 significant improvement in predictive accuracy, precision, and false-positive reduction that proves the effectiveness of the federated approach over the conventional centralized models. Moreover, surveys of clinicians show a better perception on usability and transparency, while in line with previous studies on the importance of trust artifacts in clinical AI adoption [5]. The implications are beyond the scope of autism care. With further development, the proposed system can be adapted for precision medicine applications in which patient-specific interventions are based on different, distributed datasets [6]. The combination of quantum-AI simulations [1] provides a potential route to even further personalization optimization, by speeding up the computational needs of the large-scale federated training. At the same time, linkages to AI-enabled crisis response [9,10,12] underscore the framework's possibilities when it comes to responding to critical behavioral health scenarios like escalation prevention within the 988 continuum. Future research should therefore focus on multi-center clinical validation with real-world IoT and workforce datasets using healthcare datasets to test scalability in various healthcare environments. In parallel, efforts should focus on increasing the human-centered design features [7] in order to ensure caregiver usability and equity in deployment. Taken together, these contributions highlight the fact that federated privacy-preserving AI is not only a technical innovation, but a transformational approach towards the creation of trustworthy, ethical and clinically relevant decision-support systems for the future of healthcare. References [1] Akhi, S. S., Hussain, A. H., Islam, S., Hasan, M. N., and Islam, M. M., (2023). Artificial Intelligence –Enhanced Quantum Computing for Medical Simulations: Accelerating Drug Discovery, Protein Folding, and Personalized Healthcare with Performance Optimization and Predictive Accuracy. World Journal of Advanced Research and Review, 18(01), 1328-1335 https://doi.org/10.30574/wjarr.2023.18.1.0694 [2] Islam, M. M., Hussain, A. H., Hasan, M. N., Akhi, S. S., Hussain, Hossain, M. S., and Islam, S. (2023). Fraud detection: Develop skills in fraud detection, which is a critical area of business analytics. 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