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In the evolving landscape of healthcare informatics, the integration of blockchain technology with artificial intelligence (AI) offers transformative potential for secure and intelligent health data exchange. This conceptual manuscript proposes a novel scaffold for blockchain-enhanced health data intelligence (S-BEHDI), designed as a multi-layered architectural framework that facilitates seamless, secure, and intelligent interoperability among disparate health data systems. By leveraging blockchain’s immutable ledger for data provenance and AI-driven analytics for decision support, S-BEHDI addresses critical challenges in electronic health records (EHR) exchange, such as privacy breaches, data silos, and inefficient clinical workflows. The framework incorporates a unique feedback topology that dynamically adjusts intelligence layers based on governance constraints and data exchange dynamics, ensuring robust monitoring and ethical AI deployment in clinical settings. Theoretical formulas are introduced to interpret risk propagation in data exchanges, decision confidence in AI-assisted pipelines, and governance load in interoperability frameworks. Drawing from recent peer-reviewed literature, this work synthesizes advancements in clinical AI architectures, healthcare analytics infrastructures, and interoperability models to underscore the scaffold’s theoretical underpinnings. While devoid of empirical evaluations, the conceptual design highlights implications for enhanced patient-centric care, reduced monitoring burdens, and fortified data security in precision medicine applications. Ultimately, S-BEHDI represents a forward-thinking infrastructure for fostering collaborative, intelligent health data ecosystems without compromising ethical standards or system integrity.

The integration of large language models (LLMs) into clinical decision infrastructures represents a transformative shift in healthcare delivery, enabling enhanced reasoning, data synthesis, and adaptive support for clinicians. This conceptual manuscript proposes a novel architecture, termed the adaptive LLM-orchestrated clinical ecosystem (ALOCE), designed to seamlessly embed LLMs within existing electronic health record (EHR) systems, interoperability frameworks, and governance protocols. By delineating a multi-layered structure encompassing data ingestion, semantic processing, decision augmentation, and continuous monitoring, ALOCE addresses key challenges such as data silos, ethical AI deployment, and real-time adaptability in clinical environments. Drawing on theoretical foundations from AI governance and healthcare informatics, the architecture incorporates feedback topologies for drift detection and ethical alignment, ensuring robustness in diverse clinical workflows. Conceptual formulas are introduced to model risk propagation across layers, decision confidence thresholds, and governance load balancing, providing interpretive tools for system designers. The manuscript synthesizes recent literature on clinical AI architectures, highlighting interoperability standards like FHIR and the role of LLMs in augmenting human decision-making without empirical validation. Ultimately, this work outlines a blueprint for scalable, ethical LLM integration, fostering improved patient outcomes through intelligent infrastructure orchestration. While theoretical, the implications extend to policy, deployment strategies, and future research in AI-driven healthcare systems.

The escalating prevalence of mental health crises necessitates innovative approaches to proactive intervention within longitudinal care ecosystems. This conceptual manuscript introduces the mental health crisis anticipation intelligence loop (MHCAIL), a theoretical architecture designed to integrate artificial intelligence (AI) for anticipating and mitigating crises in ongoing patient care pathways. By synthesizing clinical AI system architectures, healthcare analytics infrastructures, and electronic health record (EHR) intelligence ecosystems, MHCAIL establishes a closed-loop mechanism that processes multimodal data streams—such as EHR entries, wearable sensor inputs, and patient-reported outcomes—to generate anticipatory alerts. The framework emphasizes interoperability with existing decision support pipelines and AI governance protocols to ensure ethical deployment. Key components include predictive analytics layers for crisis risk stratification, adaptive feedback topologies for continuous system refinement, and monitoring interfaces to balance clinical workflow integration. Conceptual formulas model risk propagation dynamics and decision confidence thresholds, highlighting interpretive insights into resource allocation and governance burdens. While avoiding empirical evaluations, this work delineates theoretical implications for enhancing patient safety in mental health settings, fostering resilient longitudinal care systems that preemptively address vulnerabilities. Ultimately, MHCAIL advocates for a paradigm shift toward intelligence-driven anticipation, bridging gaps in current healthcare infrastructures to support timely, personalized interventions.

In an era of escalating healthcare demands, hospitals face persistent challenges in maintaining operational resilience amid fluctuating patient volumes, resource constraints, and unforeseen disruptions. This conceptual manuscript introduces a novel framework for real-time hospital capacity intelligence, designed to enhance decision-making through integrated AI-driven analytics and interoperable data ecosystems. Drawing on theoretical foundations from clinical AI architectures, healthcare analytics infrastructures, and decision support pipelines, the proposed system emphasizes seamless integration with electronic health records (EHRs), governance mechanisms for AI deployment, and dynamic monitoring to mitigate risks such as capacity overloads. The framework outlines a layered architecture that orchestrates data exchange, predictive analytics, and adaptive resource allocation, ensuring interoperability across clinical workflows. Key conceptual formulas are presented to interpret risk propagation in capacity management, decision confidence in real-time intelligence, and governance load in system operations. By synthesizing recent peer-reviewed literature on AI governance and clinical interoperability, this work highlights the potential for such frameworks to foster resilient hospital operations without relying on empirical data or model evaluations. Implications for healthcare systems include improved preparedness for surges, ethical AI integration, and scalable intelligence ecosystems. This theoretical exploration underscores the need for robust, AI-augmented infrastructures to support sustainable healthcare delivery.

The integration of artificial intelligence into clinical decision support systems offers improved diagnostic accuracy and efficiency, but the opacity of many machine learning models raises concerns about trust, accountability, and regulatory compliance. Explainable artificial intelligence (XAI) has been proposed to address this by making model predictions interpretable to clinicians; however, its true clinical value remains uncertain, and evaluation has not kept pace with methodological development. This systematic review aimed to identify XAI methods used in clinical decision support systems, assess how they are evaluated with clinicians, and determine whether explanations improve diagnostic accuracy, trust, mental models, and efficiency. Following PRISMA guidelines, we searched PubMed, Web of Science, IEEE Xplore, ACM Digital Library, and Scopus for studies published between 2017 and 2024. Eligible studies included original research evaluating XAI in clinical decision support systems with clinician participants and reporting quantitative or qualitative outcomes. Risk of bias was assessed using adapted QUADAS-2 and ROBIS tools, and findings were synthesized narratively with subgroup analyses. From 2,847 records, 68 studies were included. The most common XAI methods were SHAP-based feature attribution (38%), saliency or heatmap methods (29%), concept-based approaches such as TCAV (15%), and counterfactual or example-based explanations (12%). Radiology was the dominant field (54%), followed by dermatology (18%) and pathology (12%). Evaluation approaches were highly inconsistent, with few validated instruments and most studies relying on Likert-scale trust measures or qualitative feedback. Only 16% of studies showed improved diagnostic accuracy with explanations, 67% showed no significant effect, and 17% reported reduced accuracy due to over-reliance or misinterpretation. Although 82% of studies reported increased clinician trust, trust rarely correlated with actual diagnostic performance. Overall, while XAI methods are widely studied in clinical decision support, their evaluation is inconsistent and their benefits are limited. Explanations tend to increase clinician trust without reliably improving diagnostic accuracy, and may sometimes worsen performance, highlighting a trust–accuracy gap that poses important safety concerns for clinical deployment.

Clinicians often need rapid, evidence-based answers that integrate patient-specific electronic health records (EHRs) with clinical guidelines, but existing decision support tools are limited in real-time personalization. While large language models (LLMs) offer strong medical reasoning, they are prone to hallucinations and lack direct access to local EHR data, making them unsafe for standalone clinical use; meanwhile, traditional retrieval systems cannot synthesize coherent, context-aware responses. This paper proposes a retrieval-augmented generation (RAG) framework that combines dual-source retrieval from both institutional EHRs and clinical guideline databases. The system includes an EHR indexer, a guideline repository, a semantic retriever, an LLM-based generator, and a safety filter for hallucination mitigation. By grounding outputs in retrieved patient data and evidence-based recommendations, the model improves factual reliability, explainability, and clinical trustworthiness. Overall, the framework enables safe, real-time clinical question answering by integrating LLM reasoning with verified medical sources, with future validation planned on public EHR and guideline datasets.

Atrial fibrillation affects over 30 million people worldwide and requires long-term anticoagulation, with warfarin still widely used due to its efficacy and reversibility, but its narrow therapeutic window (INR 2.0–3.0) makes dosing particularly challenging, especially in high bleeding-risk patients where both under- and over-anticoagulation can lead to serious complications. Conventional dosing approaches rely on population-based nomograms and clinician judgment, failing to capture individual variability driven by genetics, diet, comorbidities, and drug interactions. To address this limitation, this article proposes a conceptual framework that integrates deep reinforcement learning with a safety-shield mechanism for personalized warfarin dosing. The system uses a deep Q-network trained on historical patient trajectories within an offline Markov Decision Process to recommend dose adjustments based on INR history and clinical risk factors, while a deterministic rule-based safety layer blocks unsafe actions, such as dose increases when INR exceeds 3.5 or extreme adjustments requiring clinician review. Conservative offline reinforcement learning further reduces the risk of unsafe policy extrapolation by limiting overestimation of out-of-distribution actions. Together, this hybrid architecture aims to improve time in therapeutic range while minimizing bleeding risk, providing a structured and clinically constrained approach for safer, individualized anticoagulation management in high-risk atrial fibrillation patients.

Chemotherapy remains a cornerstone of cancer treatment, but it is frequently associated with severe toxicities, with 30–80% of patients experiencing grade 3–4 adverse events that may require dose reduction, treatment delays, or hospitalization. While machine learning models have shown strong potential in predicting chemotherapy-related toxicities using electronic health records, genomic data, and clinical variables, most existing approaches generate only point estimates (e.g., a single risk probability) without quantifying uncertainty, limiting their clinical reliability. Such miscalibrated predictions can lead to overconfident risk underestimation or excessive caution, both of which may negatively impact treatment decisions and patient outcomes. This manuscript proposes a conceptual framework that integrates neural network-based toxicity prediction with adaptive conformal prediction to produce calibrated, patient-specific prediction intervals with formal coverage guarantees. The framework combines a feedforward neural network for risk estimation, a non-conformity score to measure how atypical a patient is relative to the training data, and an adaptive calibration mechanism that updates interval thresholds over time to reflect shifts in patient populations and clinical practice. This design enables narrower intervals for well-represented, predictable cases and wider intervals for atypical or high-uncertainty patients, thereby making prediction reliability explicit. Importantly, the method provides finite-sample coverage guarantees without requiring distributional assumptions, ensuring that true toxicity outcomes fall within the predicted intervals at a user-specified confidence level. By transforming point predictions into uncertainty-aware, clinically interpretable intervals, the framework supports more robust, risk-stratified decision-making in chemotherapy planning and moves toward safer, more trustworthy AI-assisted oncology care.