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In the realm of observational clinical systems, where electronic health records (EHRs) and real-world data dominate decision-making pipelines, robust treatment-effect estimation remains a critical challenge. This conceptual manuscript introduces the treatment effect integrity network (TEIN), a novel governance model driven by causal inference principles to orchestrate monitoring, adjustment, and validation of treatment effects within heterogeneous healthcare analytics infrastructures. By integrating causal diagrams, counterfactual reasoning, and dynamic adjustment mechanisms, TEIN addresses biases inherent in observational data, such as confounding and selection effects, without relying on empirical datasets or model training. The architecture emphasizes interoperability across clinical AI systems, facilitating seamless integration into EHR intelligence ecosystems and decision-support pipelines. Key components include a causal mapping layer for identifying potential biases, a governance orchestration module for real-time effect monitoring, and a feedback topology that propagates integrity signals through clinical workflows. Theoretical formulas are presented to interpret risk propagation in causal chains and governance load under varying observational constraints. This model advances AI governance in healthcare by providing a structured approach to maintaining the reliability of treatment effects, ultimately supporting ethical deployment in observational settings. Through literature synthesis, we highlight alignments with existing clinical AI frameworks while underscoring TEIN’s unique focus on causal-driven governance. Implications for clinical practice include enhanced decision confidence and reduced monitoring burdens in resource-constrained environments.

The escalating impacts of climate change on human health necessitate innovative approaches to integrate environmental data with clinical records for enhanced risk assessment and decision-making. This conceptual manuscript proposes the Environmental-Clinical Synergy Risk Orchestrator (ECSRO), a novel intelligence architecture designed for seamless fusion of heterogeneous data sources. Drawing on theoretical foundations in healthcare analytics and AI system infrastructure, ECSRO comprises layered components, including data ingestion gateways, fusion engines, risk intelligence cores, and governance monitors. The architecture addresses interoperability challenges by incorporating standardized exchange frameworks and adaptive governance models, ensuring ethical deployment in clinical workflows. Theoretically, it models risk propagation through interpretive formulas that capture interactions between climatic variables and clinical vulnerabilities, while emphasizing feedback topologies for continuous system refinement. Without empirical evaluations, this work synthesizes the literature on clinical AI ecosystems to highlight ECSRO’s potential to mitigate health risks exacerbated by environmental stressors, such as extreme weather events and pollution. By fostering proactive intelligence, ECSRO aims to transform reactive healthcare into anticipatory systems, promoting resilience in vulnerable populations. Future implications include scalable infrastructure for global health surveillance, underscoring the need for interdisciplinary collaboration in AI-driven integration of environmental and clinical data.

The rapid evolution of foundation models in artificial intelligence presents transformative opportunities for healthcare. Yet, their integration into domain-specific clinical analytics remains fragmented due to challenges in adaptation, interoperability, and governance. This conceptual manuscript proposes the Adaptive Clinical Integration Network (ACIN), a novel framework that facilitates seamless adaptation of foundation models for specialized clinical analytics tasks. ACIN conceptualizes a multi-layered architecture that incorporates domain-specific fine-tuning mechanisms, real-time monitoring loops, and ethical governance protocols to ensure robust integration within healthcare ecosystems. By integrating theoretical insights from clinical AI architectures, electronic health record (EHR) intelligence, and decision support systems, the framework addresses key barriers, including data heterogeneity, model drift, and regulatory compliance. We outline theoretical formulas for risk propagation in adaptation processes, decision confidence aggregation, and governance load distribution, providing interpretive tools for system designers. The implications include enhanced clinical workflow efficiency, improved interoperability across disparate analytics infrastructures, and reduced bias in AI-driven healthcare decisions. This work contributes to the theoretical foundation of AI in medicine by offering a scalable, adaptable model for future clinical analytics deployments, emphasizing ethical and infrastructural resilience without empirical validation. Ultimately, ACIN serves as a blueprint for bridging general-purpose foundation models with domain-tailored clinical applications, fostering innovation in precision medicine and population health analytics.

Traditional public health surveillance often operates within fragmented data silos, leading to delayed outbreak recognition and suboptimal clinical responses. This conceptual systems research article presents the multi-source public health surveillance intelligence mesh (MPSIM). This original architectural paradigm interconnects heterogeneous data ecosystems into a resilient, outbreak-aware intelligence fabric. MPSIM synthesizes multi-modal inputs from electronic health records, genomic repositories, environmental sensors, and social-determinant streams through a theoretically defined mesh topology that supports continuous intelligence propagation and adaptive governance.The framework introduces a five-layer stratified architecture with a unique polyadic feedback topology enabling bidirectional drift correction and resource orchestration. Three interpretive conceptual formulas are advanced to model risk propagation, decision confidence, and governance load, furnishing system designers with abstract yet operationalizable constructs.MPSIM is positioned as a blueprint for next-generation, outbreak-aware healthcare systems that embed surveillance intelligence directly into clinical workflows while satisfying stringent governance and interoperability requirements. The architecture prioritizes theoretical scalability, ethical oversight, and seamless multi-source fusion to advance proactive containment strategies across diverse deployment environments.

The integration of artificial intelligence (AI) into hospital decision ecosystems represents a transformative shift towards autonomous clinical workflows, enabling enhanced decision-making, resource optimization, and patient outcomes. This conceptual manuscript proposes a novel architecture, the Hospital Autonomous Workflow Intelligence System (HAWIS), designed to orchestrate AI-driven intelligence across clinical pipelines, electronic health records (EHRs), and governance frameworks. HAWIS incorporates layered components for data interoperability, real-time analytics, and adaptive monitoring, ensuring seamless integration within hospital environments. Drawing on recent advancements in clinical AI architectures, healthcare analytics infrastructures, and decision support systems, the architecture addresses key challenges, including interoperability barriers, governance complexities, and workflow disruptions. Theoretical formulas are introduced to model decision confidence propagation and governance load dynamics, providing interpretive tools for assessing system resilience. The framework emphasizes autonomous orchestration, where AI agents facilitate proactive interventions in hospital decision ecosystems, mitigating risks associated with data silos and regulatory compliance. By synthesizing the literature, this work highlights the need for a scalable, secure infrastructure to support AI deployment in healthcare. Ultimately, HAWIS offers a blueprint for future hospital systems, fostering intelligence-driven ecosystems that enhance clinical efficiency without empirical validation or performance metrics. This conceptual approach underscores AI’s potential to redefine hospital workflows, promoting equitable and safe decision-making.

The rapid evolution of artificial intelligence (AI) in healthcare demands innovative architectures that prioritize real-time decision-making in dynamic clinical settings. This conceptual manuscript introduces the edge-deployed hospital adaptive response topology (EHART), a novel intelligence loop designed for seamless integration into hospital ecosystems. EHART leverages edge computing to process multimodal clinical data locally, minimizing latency while ensuring interoperability with electronic health records (EHRs) and decision support systems. By orchestrating a closed-loop feedback mechanism, the framework addresses governance challenges, including AI drift monitoring, ethical data exchange, and resource-efficient analytics. Theoretical analysis highlights how EHART enhances clinical workflow resilience through adaptive intelligence cycles, reducing monitoring burdens and propagating decision confidence across interconnected nodes. Key components include layered data ingestion, real-time inference engines, and governance overlays that align with interoperability standards. Without relying on empirical evaluations, this work synthesizes recent literature on clinical AI infrastructures to propose a scalable model for smart hospitals. Implications include fostering trustworthy AI deployments in resource-constrained environments, emphasizing theoretical frameworks for risk assessment and system dynamics. Ultimately, EHART represents a paradigm for future-proofing hospital intelligence in real-time clinical contexts, balancing innovation with regulatory compliance.

Patient safety remains a paramount concern in healthcare systems, where incident narratives provide rich, unstructured evidence for identifying root causes and enhancing learning mechanisms. This conceptual manuscript introduces a novel framework for extracting root-cause themes from patient safety narratives, transforming them into structured evidence to support adaptive learning systems. Drawing on theoretical foundations in natural language processing, systems thinking, and healthcare informatics, the proposed architecture orchestrates narrative data through layered processing to uncover latent themes and propagate insights across clinical environments. By emphasizing interpretive formulas for risk propagation, decision confidence, and governance load, the framework addresses gaps in traditional analysis methods, fostering resilient healthcare infrastructures without relying on empirical data or model training. Key components include a unique layered structure for theme extraction and bidirectional feedback topologies to integrate evidence into learning cycles. The discussion explores implications for clinical deployment, data modality integration, and ethical governance, highlighting how this approach can theoretically mitigate systemic vulnerabilities. Ultimately, this work advocates for a shift toward narrative-driven, evidence-structured intelligence in patient safety, promoting proactive theme-based interventions in dynamic healthcare settings.

In the rapidly evolving landscape of digital pathology, the exponential growth of specimen data volumes poses significant challenges to timely and accurate diagnostic workflows. This conceptual manuscript introduces a novel prioritization framework designed to enhance the triage of high-risk specimens within queue-aware systems, ensuring that critical cases receive expedited review without compromising overall system integrity. Drawing on theoretical principles from systems architecture and healthcare analytics, we propose the specimen prioritization and queue intelligence network (SPQIN), a multi-layered orchestration model that integrates dynamic queue monitoring, risk assessment heuristics, and adaptive feedback topologies to mitigate bottlenecks in pathology laboratories. The framework emphasizes infrastructural resilience, incorporating interpretive formulas for risk propagation and resource allocation to optimize workflow efficiency theoretically. By synthesizing recent literature on artificial intelligence applications in digital pathology, we highlight how SPQIN addresses governance constraints, such as ethical prioritization and data modality integration, in clinical deployment environments. This work underscores the potential for queue-aware triage to transform high-risk specimen review, fostering a more responsive and equitable diagnostic ecosystem. While devoid of empirical validation, the conceptual design offers a blueprint for future infrastructural advancements in AI-driven healthcare systems, promoting theoretical discussions on scalability and interoperability.

In the evolving landscape of healthcare systems, ensuring adherence to clinical guidelines through order-sets remains a critical challenge, particularly when temporal dynamics influence decision-making processes. This conceptual manuscript introduces a novel framework for modeling guideline adherence as temporal logic constructs, enabling systematic conformance verification within order-set evaluation environments. By leveraging linear temporal logic (LTL) and computational tree logic (CTL) principles, the proposed system architecture facilitates the theoretical assessment of sequential and branching compliance pathways without relying on empirical data or simulations. Key components include a layered temporal abstraction module, a verification engine for detecting deviations in real-time clinical workflows, and a feedback topology that integrates governance constraints to mitigate potential risks. The framework emphasizes infrastructural uniqueness by incorporating a unique acronym, TCV-OS (temporal conformance verification for order-sets), with distinct layers for logic encoding, state monitoring, and adaptive reconciliation. Conceptual formulas are presented to interpret risk propagation across temporal states and decision confidence in adherence scenarios. This work synthesizes recent literature on temporal reasoning in medical decision support, highlighting gaps in current approaches and proposing architectural innovations for enhanced guideline orchestration. Ultimately, the framework offers a theoretical foundation for improving healthcare analytics integrity, fostering safer and more efficient order-set deployments in diverse clinical settings.

Nursing workload has long been recognized as a critical but under-theorized determinant of patient safety. This conceptual systems article reframes workload not as a static staffing metric but as a dynamic, measurable safety signal whose temporal and structural characteristics can be modeled to detect emerging risk states before adverse events materialize. Drawing exclusively on peer-reviewed literature published, the manuscript synthesizes evidence that elevated workload correlates with missed care, falls, medication errors, and burnout, yet existing approaches remain fragmented across isolated predictive models or retrospective acuity tools.To address this architectural gap, the article introduces the TASK-RISK framework—a novel, task-structured orchestration infrastructure that decomposes clinical activities into granular, temporally anchored units, fuses them into composite safety signals, and propagates those signals through a closed-loop detection topology. The framework is purely conceptual, specifying layer definitions, feedback mechanisms, and interpretive mathematical formalisms without empirical training or performance claims. Its five-layer architecture—task acquisition, workload quantification, signal generation, risk propagation, and governance feedback—operates entirely within existing electronic health record and sensor infrastructures, thereby offering a scalable blueprint for proactive safety governance. Theoretical implications for clinical deployment, ethical oversight, and system drift management are delineated. The manuscript establishes workload as a first-class safety signal and supplies the infrastructural scaffolding required for its integration into next-generation healthcare analytics platforms.

Antimicrobial resistance (AMR) is a major global health crisis, particularly due to multidrug-resistant gram-negative bacteria that are difficult to treat because of their impermeable outer membrane and strong efflux mechanisms, making antimicrobial peptides (AMPs) a promising alternative owing to their broad-spectrum activity and rapid bactericidal effects, although their clinical translation is limited by instability, production costs, and toxicity to human cells; meanwhile, traditional experimental discovery of AMPs is slow and expensive, and existing computational methods often optimize only antimicrobial activity while neglecting toxicity or diversity, leading to unsafe or narrow solutions, while reinforcement learning approaches may suffer from mode collapse and limited exploration of sequence space; to address these limitations, this work proposes a generative flow networks (GFlowNets)-based framework for multi-objective AMP design against gram-negative pathogens, in which a sequence generator constructs peptides stepwise and is guided by a reward function that integrates predicted antimicrobial activity and human cell toxicity from separate machine learning models, enabling simultaneous optimization of efficacy and safety; unlike conventional generative models, GFlowNets sample sequences proportional to reward, promoting diverse outputs that span the Pareto frontier of activity-toxicity trade-offs, while also allowing conditioning on desired properties and modular improvement of predictive components over time; overall, this framework provides a principled and scalable approach to antimicrobial peptide discovery that balances potency, safety, and diversity, potentially accelerating the identification of therapeutic candidates for combating antimicrobial resistance.

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide, accounting for 25–30% of deaths, particularly in low-resource settings, and early identification of high-risk patients during labor could enable timely interventions such as uterotonic administration, blood preparation, and escalation of care; however, current risk stratification models rely mainly on static antepartum factors and fail to incorporate dynamic intrapartum physiological changes. Existing tools, including those from the California Maternal Quality Care Collaborative, use baseline maternal characteristics such as prior PPH, BMI, parity, and comorbidities, but do not capture continuously evolving labor data, despite intrapartum signals like fetal heart rate patterns, maternal vital sign trends, and labor progression metrics containing rich predictive information that remains underused in real-time decision-making, while clinical judgment is limited by inter-observer variability and inability to integrate complex temporal trends. To address this gap, we propose an explainable gradient boosting machine framework for real-time PPH risk prediction that integrates electronic fetal monitoring parameters (baseline rate, variability, decelerations), maternal vital signs (heart rate, blood pressure, temperature, oxygen saturation), and labor progression features (cervical dilation, contraction frequency, stage duration, and oxytocin use), producing continuously updated risk scores throughout labor. The system combines a gradient boosting model (XGBoost or LightGBM), a SHAP-based explainability module, a real-time feature extraction pipeline, and a clinician-facing dashboard that displays risk scores and key contributing factors, where SHAP provides both global and patient-specific interpretability by identifying how features such as tachysystole or prolonged labor stages influence predictions, thereby improving transparency and clinical trust. Overall, this framework enables dynamic, interpretable PPH risk assessment using routinely collected intrapartum data, combining predictive accuracy with explainability to support earlier detection of hemorrhage risk and more timely, targeted interventions.

During the COVID-19 pandemic, machine learning models developed in high-resource hospitals achieved strong performance in predicting outcomes such as mortality, ICU admission, and mechanical ventilation, but their accuracy often degrades when applied to low-resource settings due to differences in patient populations, disease severity, clinical practices, and documentation quality. Low-resource hospitals also face limited patient volumes, incomplete labeled data, and strict privacy regulations (e.g., HIPAA and GDPR), which prevent centralized data sharing and hinder independent model development, creating a barrier to equitable AI deployment. To address this, we propose a federated transfer learning framework that adapts prognostic models from high-resource to low-resource hospitals without exchanging patient-level data. The approach transfers only aggregate statistics (e.g., feature means, variances, class-conditional distributions, and correlations) via a secure lightweight protocol, enabling target hospitals to align feature distributions using domain adaptation techniques and fine-tune models on small local datasets. The framework includes source model training, statistical aggregation, secure transmission, and target-side adaptation modules, ensuring no raw patient data leaves any institution. By relying on aggregate statistics, the method preserves privacy while mitigating domain shift and maintaining clinical utility across diverse healthcare environments. This scalable and privacy-preserving framework supports broader deployment of COVID-19 predictive models and provides a generalizable strategy for other medical conditions with heterogeneous healthcare settings.

Surgical site infections (SSIs) remain a significant source of postoperative morbidity despite established guidelines for perioperative antibiotic prophylaxis. Current protocols emphasize fixed preoperative timing and interval-based intraoperative redosing, yet fail to account for patient heterogeneity, pharmacokinetic variability, and uncertainty in procedure duration. This study proposes a hierarchical reinforcement learning (HRL) framework for personalized optimization of antibiotic prophylaxis across the perioperative timeline. The framework decomposes decision-making into two coordinated levels: a high-level policy that determines optimal preoperative antibiotic timing based on predicted procedure duration and patient-specific infection risk, and a low-level policy that adaptively manages intraoperative redosing using real-time updates on elapsed time, remaining duration, and cumulative drug exposure. Procedure duration is estimated using machine learning models that provide both point predictions and uncertainty intervals, enabling risk-sensitive decision-making. The problem is formalized as a Markov decision process with a reward structure balancing SSI prevention against antibiotic stewardship, incorporating penalties for unnecessary dosing and suboptimal timing. Off-policy evaluation using historical surgical data is proposed to assess performance relative to guideline-based and clinician-driven strategies. By integrating predictive modeling with multi-timescale decision optimization, the framework aims to reduce SSI incidence while minimizing antibiotic overuse. This approach highlights the potential of reinforcement learning to advance precision perioperative care and improve clinical outcomes through adaptive, data-driven prophylaxis strategies.

Intensive care unit (ICU) data consist of high-frequency multivariate time series, including vital signs, laboratory results, and hemodynamic variables, which are crucial for clinical decision-making and predictive modeling. However, these data are frequently incomplete due to monitor interruptions, clinical workflows, and selective measurement, with missing rates ranging from 20% to over 80% depending on the variable. Missingness in ICU time series is often not random, as sicker patients tend to be monitored more frequently, creating a missing not at random (MNAR) mechanism. Conventional imputation methods such as mean filling, interpolation, and multiple imputation assume random missingness and therefore introduce bias and distort clinical signals under MNAR conditions. We propose a variational recurrent neural network (VRNN) with stochastic attention to impute ICU time series under MNAR settings. The framework integrates latent state modeling of physiological dynamics, stochastic attention over observed measurements using Gumbel-Softmax sampling, and a missingness pattern encoder that explicitly models the observation process. An imputation decoder generates probabilistic estimates of missing values conditioned on latent states, attention context, and missingness structure. This framework enables uncertainty-aware and potentially unbiased imputation in ICU time series by jointly modeling physiological dynamics and missingness mechanisms. It combines variational inference and stochastic attention to address systematic bias in conventional approaches, with future work needed to validate performance on real-world ICU datasets.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death, with exacerbations worsening functional decline, reducing quality of life, and increasing healthcare use. Current management remains reactive, with treatment often initiated only after symptoms worsen. Existing monitoring approaches struggle to distinguish between clinically significant deterioration and normal variability, leading to delayed intervention. This article proposes a digital twin framework combining patient-specific respiratory models with real-time wearable data to predict and manage COPD exacerbations proactively. The framework includes a mechanistic lung model, continuous data ingestion, a data assimilation module, an exacerbation prediction layer, and an alert system, enabling early detection of physiological deviations before severe symptoms arise. By supporting pre-emptive telehealth, medication adjustments, and patient self-management with clinician oversight, this approach could shift COPD care from reactive to personalized, proactive management, pending robust modeling, reliable sensing, and real-world validation.

This article proposes a conceptual framework for a diagnostic support system in emergency departments that leverages large language models, retrieval-augmented generation, and chain-of-thought reasoning. By combining triage notes and vital signs, the system generates a ranked differential diagnosis list to assist clinicians without replacing their judgment. The framework includes components like a triage note encoder, a vital sign encoder, a retrieval module, and a diagnosis ranker, using evidence from clinical guidelines, curated references, and de-identified prior cases. The approach grounds the model in authoritative knowledge while ensuring transparency and explainability in the diagnostic process. However, prospective validation, integration into workflows, and clinician oversight are crucial before implementation to ensure safety and effectiveness.

Detecting rare diseases often requires data from multiple institutions due to the scarcity of cases at individual hospitals. Centralizing data is not feasible due to privacy, consent, and jurisdictional issues. Federated learning enables model training across hospitals without transferring raw data, but it lacks formal privacy guarantees. Model updates can still leak information, and aggregation servers may compromise privacy if they handle unprotected data. This article presents a conceptual framework combining federated learning, differential privacy, and secure multi-party computation for rare disease detection across 50+ international hospitals. The system addresses data scarcity, regulatory fragmentation, and network heterogeneity. Each hospital trains a local model, applies differential privacy to updates, and shares encrypted updates via an aggregation protocol. Non-colluding servers compute global updates without accessing plaintext data. Differential privacy reduces the impact of individual patient data, while secure multi-party computation ensures privacy at the aggregation layer. These methods enable a privacy-preserving approach to federated learning for rare disease collaboration. The proposed framework enables multi-continental rare disease detection without centralizing patient data, offering a privacy-preserving model for future consortia.

Chest X-ray is a commonly used imaging tool in both acute and routine care, but the increasing reporting workload highlights the need for structured preliminary reports that aid triage, reduce delays, and ensure clinical relevance. Current AI systems often focus on classification or generic report generation, neglecting critical factors like free-text radiology requests, clinical history, and comparison context, leading to reports that, while technically fluent, are insufficiently focused. This article proposes a multimodal vision-language model that interprets both chest X-ray images and free-text radiology requests to generate structured preliminary reports directly addressing the clinical question. The model combines a radiographic encoder based on vision transformers, a text encoder for requests and prior reports, a cross-modal attention module, and a structured report decoder, organizing the output into relevant sections such as indication, technique, findings, impression, comparison, and answer-to-request. By aligning report generation with the clinical request, the model ensures that it answers specific questions—such as concerns about pneumonia, pulmonary oedema, or pneumothorax—improving report relevance, reducing misinterpretation, and supporting safer human-in-the-loop review. However, its effectiveness relies on accurate alignment, factual consistency, uncertainty management, and validation in real-world radiology settings.

Extracorporeal membrane oxygenation (ECMO) is used to support patients with severe cardiac or respiratory failure, requiring constant manual adjustments of pump flow, sweep gas flow, and oxygen fraction. However, current ECMO management lacks a real-time optimization system tailored to individual patient needs. This manuscript proposes an offline reinforcement learning framework for dynamic ECMO optimization, utilizing real-time measurements of blood gases, hemodynamics, and pump flow. The framework includes a state encoder for various patient data, an action space for adjustments to ECMO settings, and a reward function that balances oxygenation, hemodynamic support, and complication avoidance. A safety shield filters unsafe recommendations before clinician review. The system aims to provide personalized, proactive, and safety-constrained ECMO management, with the goal of guiding future research validation rather than claiming experimental results.

Emergency department chief complaints and triage notes are early indicators of health changes during infectious disease outbreaks. These records, made before confirmatory testing, provide a presyndromic view of population health. Traditional syndromic surveillance relies on predefined syndrome categories, which may not align with novel pathogens. Early outbreaks often present as sparse, ambiguous symptom clusters, resulting in few labeled examples for automated detection. This framework suggests using contrastive learning with prototypical networks for few-shot detection of emerging infectious disease syndromes from free-text notes. It leverages historical data to create a robust clinical text embedding space, with a small set of labeled examples defining new syndromes. The system includes a contrastive pre-training encoder, prototypical network, and few-shot classifier. The encoder learns from unlabelled historical notes, and the prototypical network creates syndrome prototypes from a few labeled examples. This framework is designed for situations where public health officials observe early suspect cases but lack mature labeled datasets. It can identify early clusters by comparing incoming notes to emerging syndrome prototypes. Contrastive learning with prototypical networks enables proactive presyndromic surveillance, allowing rapid adaptation during the early phase of an outbreak without relying on large labeled datasets.

Pressure ulcers are a persistent issue in bedridden patients, especially in intensive care, rehabilitation, and long-term care, leading to pain, infection, and extended hospital stays. Current risk assessments rely on intermittent scoring and clinical judgment, failing to account for continuous changes in body posture, tissue loading, and mechanical tolerance. This conceptual framework proposes a physics-informed graph neural network to predict pressure ulcer risk by integrating data from body position sensors, local tissue loading, and skin perfusion measurements into a dynamic, personalized model. The model represents the body as a graph, with nodes representing pressure-prone areas and edges indicating anatomical and mechanical connections. Tissue stress, perfusion data, and posture features are processed through network layers constrained by soft-tissue mechanics. By encoding the relationship between external forces, internal tissue deformation, ischemia, and damage, the framework allows risk propagation across adjacent anatomical regions. This approach offers a path for continuous, personalized pressure ulcer risk monitoring, laying the foundation for clinical validation and sensor integration.

Rare pediatric tumors like sarcomas, neuroblastoma, medulloblastoma, and retinoblastoma pose a challenge for developing deep learning models due to the limited availability of histopathology images, which are distributed across multiple institutions. This scarcity is compounded by privacy concerns, as whole-slide images often contain sensitive clinical and genomic data, and generative adversarial networks (GANs) risk memorizing and leaking training samples. To address this, a differentially private GAN framework is proposed for synthesizing high-resolution histopathology patches of rare pediatric cancers. The framework incorporates a generator for image synthesis, a discriminator for realism assessment, per-sample gradient clipping, Gaussian noise injection, and a privacy accountant, ensuring provable privacy guarantees during the training process. The synthetic images generated can aid in data augmentation, model pre-training, and benchmarking without exposing identifiable pathology data, offering a privacy-preserving solution for dataset augmentation while emphasizing the importance of clinical validation.

Anticoagulation management requires balancing multiple factors such as bleeding risk, thromboembolic risk, drug interactions, and renal function. Deep learning can assist in risk prediction, but its effectiveness relies on clinicians' ability to understand and verify the recommendations. Black-box models may recommend actions without providing clear explanations. In contrast, clinical guidelines are rule-based but not directly executable by neural models. This article introduces a neuro-symbolic XAI framework that combines deep learning predictions with explicit clinical guidelines. It includes a neural prediction module, a symbolic reasoning engine, and an integration layer for traceable justifications. The neuro-symbolic approach connects data-driven predictions to clinical rules, improving auditability and trustworthiness in decision support. This framework aims to enhance anticoagulation management by providing verifiable, clinician-understandable decision support, focusing on explainability-by-design.

Fall risk in aging populations is a modifiable health concern, with mobility patterns changing over time due to factors like frailty, comorbidities, and medication. Smartwatch accelerometers provide a privacy-sensitive way to monitor gait and movement outside clinical settings. However, federated learning, which supports privacy by keeping sensor data local, faces challenges in aging populations due to concept drift from gradual mobility decline, which can invalidate static models. This article proposes a federated continual learning framework to adaptively maintain fall risk prediction models using smartwatch data. The system includes local models that combine feature extraction with temporal sequence modeling, continual learning to prevent forgetting, and a federated server for privacy-preserving coordination. It aims to support personalized fall risk monitoring, reduce concept drift, and enable scalable deployment in senior care settings, with clinical validation necessary for real-world assessment.

Delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage is a significant cause of morbidity, mortality, and long-term neurological disability. Current clinical scores like WFNS, Hunt-Hess, and modified Fisher scale provide useful baseline risk information but often fail to capture subtle multi-hour deteriorations. Standard recurrent models can process sequential data but struggle with long-term dependencies and do not offer clinicians useful uncertainty information. To address this, a hierarchical Transformer model is proposed for DCI prediction, leveraging short-term hourly changes and longer multi-day trends in neurological and vital sign data. The model incorporates an uncertainty-aware attention mechanism to minimize the impact of unreliable or missing data and generates risk-stratified alerts with confidence levels. This approach aims to offer an explainable, clinically actionable tool that supports early recognition of DCI while ensuring clinician oversight. Future work will involve retrospective development and prospective validation to enhance its clinical utility.

Personalized rehabilitation exercise prescriptions are essential for recovery after neurological injury, orthopedic surgery, and chronic decline. While physical therapists have valuable expertise, translating it into scalable computational systems is challenging. Standard deep reinforcement learning relies on manually defined reward functions, but in rehabilitation, clinically significant goals like movement quality, fatigue, pain, safety, motivation, and adherence are difficult to quantify. This paper introduces a framework combining inverse reinforcement learning (IRL) and deep reinforcement learning (DRL) to learn personalized rehabilitation prescriptions from therapist demonstrations. IRL would derive expert-aligned rewards, and DRL would use these to create adaptive exercise plans. The framework encompasses therapist demonstration collection, movement trajectory representation, reward inference, policy learning, safety constraints, and clinical oversight. Demonstrations would include exercise selection, progression decisions, and therapist responses to patient fatigue, pain, or adherence issues. IRL could capture implicit clinical priorities, while DRL would adjust prescriptions based on patient conditions such as fatigue, progress, and engagement. The framework aims to create scalable, personalized rehabilitation prescriptions, offering a conceptual model for future rehabilitation robotics, exergaming, and home-based digital rehabilitation systems.