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The rapid evolution of artificial intelligence (AI) in healthcare necessitates innovative frameworks to optimize hospital operations. This conceptual manuscript proposes the Digital Twin-Enabled Operations Resilience Architecture (DTORA), a novel intelligence framework that leverages digital twins to simulate, monitor, and enhance hospital operational dynamics. DTORA integrates real-time data from electronic health records (EHRs), clinical workflows, and interoperable systems to create virtual replicas of hospital processes, enabling predictive analytics and decision support without empirical testing. The framework’s layered structure includes a simulation core, intelligence orchestration layer, and governance feedback loop, addressing challenges in resource allocation, workflow efficiency, and risk mitigation. By synthesizing recent literature on clinical AI architectures and healthcare analytics infrastructures, DTORA emphasizes theoretical interoperability, AI governance, and human-AI integration. Conceptual formulas model risk propagation, decision confidence, and monitoring burden, providing interpretive tools for system design. This work highlights the potential of digital twins to transform hospital intelligence ecosystems, fostering resilient operations amid data complexities and regulatory demands. While theoretical, DTORA offers a blueprint for future deployments, underscoring the need for ethical monitoring and seamless integration in diverse clinical settings.

The integration of graph-based architectures into healthcare systems represents a pivotal advancement, enabling personalized clinical intelligence through patient similarity metrics. This conceptual manuscript proposes a novel framework, the Graph-Integrated Patient Affinity Network (GIPAN), that orients patient data as interconnected nodes within a dynamic graph, facilitating similarity-driven insights for clinical decision-making. Drawing from theoretical foundations in clinical AI infrastructures, electronic health record (EHR) ecosystems, and interoperability frameworks, GIPAN emphasizes layered graph embeddings that capture multidimensional patient profiles, including temporal trajectories, comorbidity patterns, and treatment responses. The architecture incorporates feedback loops for adaptive similarity refinement, ensuring alignment with evolving clinical workflows without empirical validation. Key theoretical contributions include formulas for similarity propagation across graph layers and governance load estimation in deployment scenarios. By synthesizing recent literature on graph neural networks in healthcare analytics and decision-support pipelines, this work highlights the infrastructural prerequisites for scalable, privacy-preserving patient matching. Potential impacts encompass enhanced diagnostic precision in heterogeneous populations and streamlined resource allocation in personalized medicine ecosystems. This conceptual design underscores the need for robust AI governance to mitigate biases in similarity computations, paving the way for future theoretical explorations in graph-centric clinical intelligence.

 The integration of artificial intelligence (AI) into healthcare systems has transformative potential to enhance patient outcomes, particularly in managing chronic conditions by improving medication adherence. This conceptual manuscript proposes a novel intelligence loop embedded within pharmacy-electronic health record (EHR) interoperability networks to orchestrate real-time adherence monitoring and intervention. Drawing on theoretical architectures from clinical AI systems, healthcare analytics infrastructures, and decision support pipelines, we delineate a closed-loop framework that leverages data exchange standards to facilitate seamless information flow between pharmacies and EHR platforms. The loop incorporates predictive analytics for adherence risk stratification, automated alerts for clinicians, and adaptive feedback mechanisms to refine interventions over time. Key considerations include governance protocols to ensure data privacy, ethical AI deployment, and mitigation of interoperability challenges such as semantic inconsistencies. Through a synthesis of recent literature, we explore how this intelligence loop could redistribute clinical workflows, reducing non-adherence-related complications while optimizing resource allocation in interconnected health ecosystems. Conceptual formulas model decision confidence, propagate confidence, and assess governance load sensitivities, providing interpretive tools for system design. Ultimately, this work advances theoretical discourse on AI-orchestrated adherence strategies, emphasizing infrastructural resilience and human-AI collaboration in pharmacy-EHR networks.

The integration of multimodal data sources in oncology, particularly imaging and electronic health records (EHRs), offers significant opportunities to advance precision medicine through sophisticated analytics architectures. This conceptual manuscript proposes a novel multimodal oncology integration framework (MOIF) to orchestrate seamless data fusion, analytical processing, and decision support within integrated imaging-EHR ecosystems. Drawing on theoretical foundations from clinical AI system architectures and healthcare analytics infrastructures, the framework emphasizes interoperability, governance, and monitoring to address challenges in data heterogeneity, privacy, and clinical workflow integration. By synthesizing recent literature on EHR intelligence ecosystems and decision support pipelines, we outline the architectural layers, including data ingestion, fusion, analytics, and feedback mechanisms, to enable real-time insights for oncology care. Conceptual formulas are introduced to model risk propagation, decision confidence, and governance load, providing interpretive tools for system dynamics. The architecture aims to enhance clinical decision-making by facilitating multi-modal data exchange and AI-driven analytics without empirical evaluations. Potential impacts include improved interoperability in oncology settings, reduced decision latency, and robust governance for deployment. This work contributes to the discourse on AI infrastructures in healthcare, offering a blueprint for future conceptual developments in integrated oncology ecosystems.

The integration of multi-modal data sources in healthcare represents a pivotal advancement for enhancing diagnostic precision and clinical decision-making. This conceptual manuscript proposes a novel architectural framework, termed the diagnostic fusion intelligence lattice (DFIL), designed to orchestrate the seamless fusion of imaging modalities—such as MRI, CT, and X-ray—with structured clinical data from electronic health records (EHRs). By emphasizing interoperability, governance, and workflow integration, DFIL addresses the challenges of data heterogeneity, diagnostic latency, and human-AI collaboration in clinical environments. The framework incorporates layered structures for data ingestion, fusion orchestration, and decision augmentation, incorporating feedback topologies to mitigate diagnostic drift and ensure ethical oversight. Theoretical analyses explore operational dynamics, including risk propagation models and governance sensitivities, without empirical validation. Drawing on recent literature in clinical AI architectures and healthcare analytics, this work synthesizes insights into how such systems could transform diagnostic pipelines in settings like oncology, neurology, and cardiology. Key contributions include conceptual formulas for fusion confidence and resource allocation, highlighting trade-offs in multi-modal integration. Ultimately, DFIL offers a blueprint for future AI-driven diagnostic ecosystems, promoting safer, more efficient healthcare delivery through theoretical infrastructural innovation.

The integration of healthcare analytics across regional boundaries remains a critical challenge in modern population health management, where disparate data ecosystems hinder comprehensive intelligence generation. This conceptual manuscript proposes the population health intelligence mesh (PHIM), a novel architectural framework designed to facilitate seamless cross-regional analytics integration through a mesh-based topology that emphasizes interoperability, governance, and real-time decision support. Drawing from theoretical foundations in clinical AI architectures and healthcare informatics, PHIM conceptualizes a layered structure comprising data ingestion nodes, federated analytics hubs, and adaptive governance overlays to mitigate silos in electronic health record (EHR) systems and enable population-level insights. Key components include decentralized intelligence propagation mechanisms and feedback loops for dynamic system adaptation, ensuring resilience in diverse healthcare environments. Theoretical formulas are introduced to interpret risk propagation across regions, decision confidence aggregation, and governance load distribution, highlighting potential operational efficiencies without empirical validation. The framework addresses interoperability frameworks by synthesizing recent literature on AI governance and workflow integration, offering a blueprint for theoretical advancements in population health analytics. While focusing on conceptual viability, PHIM underscores the need for ethical monitoring and human-AI collaboration in cross-regional deployments, paving the way for future infrastructural innovations in healthcare systems.

In the complex ecosystem of perioperative healthcare systems, where electronic health records (EHRs), real-time monitoring devices, and clinical decision support tools intersect, the management of surgical complication risks demands robust analytics infrastructures. Perioperative analytics systems leverage artificial intelligence (AI) to process multimodal data streams, including patient demographics, intraoperative variables, and postoperative indicators, aiming to enhance clinical outcomes while mitigating adverse events such as anastomotic leaks, infections, and venous thromboembolism. However, existing approaches often fragment risk assessment across isolated phases, lacking a cohesive lifecycle perspective that integrates data acquisition, model deployment, workflow embedding, and ongoing governance. This conceptual gap hinders seamless interoperability, privacy preservation, and safety assurance in high-stakes surgical environments. To address this, we introduce the Surgical Complication Risk Lifecycle Architecture (SCRiLA). This novel framework conceptualizes risk management as a cyclical process encompassing data harmonization, predictive modeling, decision integration, and feedback-driven oversight. SCRiLA emphasizes structural layers for handling EHR interoperability challenges, bias mitigation in analytics pipelines, and clinician-AI collaboration in perioperative workflows. Implications for deployment include improved system resilience against data drift, enhanced accountability in risk predictions, and streamlined governance protocols that align with regulatory standards, ultimately fostering safer and more efficient perioperative care delivery. By framing surgical complication risks through a lifecycle lens, this architecture provides interpretive insights for informatics stakeholders to optimize analytics systems without empirical validation.

In the high-stakes domain of intensive care units (ICUs), where patient conditions evolve rapidly through continuous streams of physiological signals, there is a pressing need for advanced intelligence frameworks that can interpret temporal patterns without relying on empirical data processing. This conceptual manuscript proposes the temporal signal adaptive resonance topology (TSART), a novel architectural design for orchestrating signal intelligence in continuous ICU monitoring environments. TSART integrates layered modules for signal temporality capture, adaptive resonance mapping, and feedback-driven orchestration, emphasizing theoretical interoperability with electronic health records (EHRs) and decision support pipelines. By synthesizing recent literature on clinical AI architectures and healthcare analytics infrastructures, we outline how TSART addresses governance challenges, such as drift sensitivity and resource allocation, through interpretive formulas modeling decision latency and monitoring burden. The framework fosters seamless clinical workflow integration, mitigating human-AI interaction frictions in real-time environments. Without empirical validations, this work highlights theoretical implications for enhancing ICU vigilance, including reduced cognitive overload for clinicians and optimized signal governance. Ultimately, TSART represents a blueprint for future intelligence ecosystems that prioritize temporal fidelity and systemic resilience in critical care settings.

The rapid evolution of artificial intelligence in healthcare necessitates robust infrastructures capable of integrating advanced computational models into clinical workflows. This conceptual manuscript proposes a transformer-embedded clinical phenotyping infrastructure model, designed to enhance the extraction and utilization of patient phenotypes from electronic health records (EHRs) through transformer-based architectures. By embedding transformer mechanisms within a multi-layered infrastructure, the model facilitates dynamic phenotyping, enabling precise patient stratification and decision support without relying on empirical data or performance metrics. The framework emphasizes interoperability, governance, and seamless integration with existing healthcare analytics ecosystems, addressing challenges in data exchange and AI deployment. Key components include a phenotypic encoding layer, a transformer orchestration module, and a feedback loop for continuous refinement. Conceptual formulas are introduced to interpret risk propagation in phenotyping errors, decision confidence in clinical outputs, monitoring burdens on system resources, resource allocation for computational efficiency, governance loads in regulatory compliance, and sensitivity to data drift. This model contributes to theoretical discussions on AI-driven healthcare systems by outlining an architecture that prioritizes ethical deployment and clinical utility. Through literature synthesis, it draws on recent advancements in clinical AI architectures and EHR intelligence, positioning the infrastructure as a foundational element for future intelligent health systems. The implications extend to improved clinical phenotyping accuracy and infrastructure resilience in diverse healthcare settings.

In the evolving landscape of healthcare systems, fraudulent claims pose significant threats to resource integrity and patient care equity. This conceptual manuscript introduces a novel anomaly-responsive claims governance infrastructure (ARCGI), designed as an intelligence architecture that integrates anomaly awareness with fraud governance mechanisms. Drawing from theoretical foundations in clinical AI architectures and healthcare analytics, the ARCGI emphasizes proactive detection, adaptive monitoring, and ethical oversight without relying on empirical data or model training. The framework comprises layered components for data ingestion, anomaly profiling, intelligence orchestration, and governance feedback loops, ensuring interoperability with electronic health records (EHR) ecosystems and decision support pipelines. Conceptual formulas articulate risk propagation dynamics, decision confidence thresholds, and governance load distributions, highlighting interpretive pathways for mitigating fraud in claims processing. By synthesizing recent literature on AI governance and interoperability frameworks, this work underscores the architectural imperatives for anomaly-aware systems in healthcare claims environments. The ARCGI advances theoretical discourse on fraud governance by proposing unique topologies for feedback and resource allocation, fostering resilient infrastructures that align with clinical workflow integrations. Ultimately, this architecture offers a blueprint for enhancing fraud governance through intelligent, anomaly-centric designs, promoting sustainable healthcare analytics without performance metrics or experimental validations.

The integration of artificial intelligence (AI) into in-hospital clinical decision systems has revolutionized patient care, yet challenges persist in ensuring explainability, managing risks, and establishing robust governance. This conceptual manuscript proposes the explainable risk governance orchestration framework (ERGOF), a novel model designed to orchestrate risk intelligence within clinical environments. ERGOF emphasizes layered architectures that integrate data interoperability, real-time risk assessment, explainable decision pipelines, and adaptive governance mechanisms to mitigate biases and enhance trustworthiness. Drawing from theoretical foundations in healthcare informatics and AI ethics, the framework addresses key gaps in current systems, such as opaque decision-making and fragmented oversight. Through interpretive formulas for risk propagation and governance load, ERGOF illustrates how explainable intelligence can be embedded in clinical workflows without empirical validation. The model promotes seamless integration with electronic health records (EHRs) and decision support tools, fostering human-AI collaboration in high-stakes settings like intensive care units. By prioritizing transparency and accountability, ERGOF offers a pathway for sustainable AI deployment in hospitals, potentially reducing clinical errors and improving outcomes. This work synthesizes recent literature to advocate for governance-centric designs, highlighting the need for interdisciplinary approaches in AI-driven healthcare. Ultimately, ERGOF serves as a blueprint for future systems that balance innovation with ethical imperatives in clinical decision-making.

The integration of artificial intelligence (AI) into healthcare systems has revolutionized clinical analytics, enabling enhanced diagnostic accuracy, predictive modeling, and personalized treatment pathways. However, the opacity of many AI models poses significant challenges to their clinical adoption, necessitating advancements in explainable AI (XAI) to ensure interpretability and transparency. This narrative review synthesizes the literature on XAI within clinical systems, focusing on interpretability mechanisms, transparency frameworks, and deployment constraints in healthcare analytics. Drawing from high-impact studies, we examine how XAI addresses the “black box” nature of machine learning models in high-stakes medical decisions, particularly in contexts where performance has traditionally been prioritized over explainability. Key themes include the shift toward inherently interpretable models for critical applications, such as diagnostic imaging and predictive analytics, where post-hoc explanations often fall short. We explore the ethical imperatives for responsible AI deployment, including strategies for mitigating harm through transparent systems that align with clinical workflows. The review integrates perspectives on XAI in clinical diagnostics, emphasizing challenges in balancing model complexity with user trust. Transparency is framed not merely as a technical feature but as a systemic requirement, incorporating structured reporting practices for AI interventions and standardized modeling approaches. Deployment constraints are analyzed through the lens of real-world integration, including regulatory considerations, data privacy concerns, and human–AI interaction dynamics in healthcare infrastructures. We synthesize evidence from diverse applications, such as lung cancer diagnosis via explainable models and radiographic assessments, underscoring the need for multidisciplinary approaches to XAI. Furthermore, the review highlights biases in AI systems, particularly sex and gender disparities, and advocates for inclusive analytics to foster equitable healthcare. Clinical applications beyond the black box are discussed, with calls for standardized reporting to enhance reproducibility and trust. We position XAI as essential for closed-loop systems that incorporate feedback mechanisms, ensuring ongoing model recalibration in dynamic clinical environments. The synthesis reveals persistent gaps in current XAI deployments, such as overreliance on surrogate explanations that may mislead clinicians. Ultimately, this review proposes a systems-level framework for XAI in healthcare, integrating data ingestion, inference, decision support, and governance loops to overcome transparency barriers. This comprehensive overview informs the development of future AI-enabled healthcare infrastructures, emphasizing interpretability as a cornerstone for safe and effective clinical analytics.

The integration of multi-modal intelligence in healthcare represents a transformative paradigm, where artificial intelligence (AI) systems synthesize diverse clinical data streams—ranging from electronic health records (EHRs), imaging, genomics, and wearable sensor data—to enable more cohesive, predictive, and actionable insights. This narrative review synthesizes recent advancements in AI for healthcare systems and analytics, focusing on conceptual integration patterns that bridge disparate data modalities to enhance clinical decision-making and system-level efficiencies. We explore how multi-modal AI frameworks address the heterogeneity of healthcare data, fostering intelligent systems that support precision health, risk stratification, and closed-loop interventions. Key themes include the evolution of multi-modal machine learning techniques, such as fusion models that combine radiological imaging with clinical parameters for improved diagnostic accuracy, and the role of large language models (LLMs) in processing unstructured textual data alongside structured metrics. For instance, integrated frameworks leverage deep residual networks and transformers to handle multimodal inputs, enabling applications in areas like pulmonary hypertension prediction and Alzheimer’s disease progression forecasting. We highlight systems-level architectures that incorporate feedback loops for continuous model refinement, emphasizing the need for robust data modeling in federated learning environments to ensure privacy and interoperability across healthcare infrastructures. Challenges in data fusion, such as handling dataset shifts and ensuring equitable access to digital health tools, are contextualized within broader analytics pipelines. The review underscores original synthesis logic by framing integration patterns through a systems lens: data ingestion, intelligent inference, decision support, and governance. This approach reveals how multi-modal AI not only amplifies analytic capabilities but also redefines healthcare delivery models, from virtual biopsies using mammography data to comprehensive communication skills training for physicians via AI-driven video analysis. Ultimately, this synthesis positions multi-modal intelligence as a cornerstone for next-generation healthcare systems, promoting seamless interoperability and human-AI collaboration. By avoiding empirical benchmarks and focusing on conceptual patterns, we provide an interpretive framework that guides future deployments, ensuring AI enhances rather than disrupts clinical workflows.

The integration of artificial intelligence (AI) into healthcare systems has transformed population health analytics, enabling scalable infrastructures that process vast datasets to inform clinical decisions, resource allocation, and policy-making. This narrative review synthesizes recent literature on AI system architectures and governance models, focusing on how these elements underpin analytics-driven healthcare ecosystems. We examine the evolution of AI-enabled infrastructures, emphasizing federated learning, explainable models, and ethical frameworks to address data privacy, interoperability, and equity in population-level analytics. Key architectures include vertically integrated systems that streamline data ingestion, model deployment, and real-time inference, as seen in federated approaches that mitigate data silos while preserving patient confidentiality. Governance models are critical for ensuring trustworthy AI deployment, incorporating regulatory oversight, ethical principles adapted from military contexts to healthcare, and consensus-based guidelines for prediction models. We highlight the role of blockchain and data trusts in enhancing transparency and consent mechanisms, particularly in global health responses to pandemics and chronic disease management. The review structures the discourse around systems-level framing, integrating data flows, algorithmic decision support, and closed-loop feedback mechanisms that adapt to clinical outcomes. For instance, electronic health record (EHR)-based prediction models facilitate acute illness forecasting and outcome prediction in conditions like rheumatoid arthritis and oncology. We propose an original synthesis logic that conceptualizes AI infrastructures as adaptive networks, where governance acts as a regulatory layer overlaying architectural components to balance innovation with risk mitigation. Challenges such as bias in commercial datasets and the need for international cooperation are noted, but the emphasis remains on infrastructural resilience. Ultimately, this synthesis underscores the imperative for hybrid human-AI systems that prioritize population health equity, with governance models evolving to support sustainable analytics infrastructures. By positioning AI as a foundational tool for healthcare transformation, the review advocates for interdisciplinary collaboration to refine these systems, ensuring they deliver actionable insights while upholding ethical standards in diverse healthcare settings.

Head and neck cancer radiotherapy requires highly precise dose delivery to ensure tumor control while sparing nearby critical structures, but daily anatomical changes such as tumor shrinkage, weight loss, and setup variability often degrade treatment accuracy. Although cone-beam CT provides valuable daily imaging, current adaptive radiotherapy workflows remain largely manual, time-consuming, and infrequent, limiting their ability to respond to ongoing anatomical changes and often resulting in suboptimal target coverage or increased toxicity risk. To address these limitations, we propose a deep reinforcement learning framework for fully automated daily treatment adaptation using cone-beam CT and dosimetric constraints. The problem is formulated as a sequential decision-making task in which an agent adjusts beam parameters based on evolving patient anatomy, cumulative dose, and constraint satisfaction. The state includes daily imaging and dose history, the action space involves fluence or multileaf collimator adjustments, and the reward function balances target coverage, organ-at-risk sparing, and plan stability. A patient-specific simulator based on historical imaging enables training without real-time patient interaction. This framework enables continuous, personalized, and automated plan adaptation that directly responds to anatomical changes while maintaining clinical safety constraints. By leveraging long-horizon optimization, the system can outperform static planning strategies and better manage stochastic anatomical variations in head and neck cancer treatment. Overall, this approach provides a foundation for closed-loop adaptive radiotherapy that could improve treatment accuracy, reduce toxicity, and reduce reliance on manual planning.

Immunotherapy with immune checkpoint inhibitors is a standard treatment for advanced non-small cell lung cancer (NSCLC), with durable responses in selected patients. Whole-slide histopathology images provide morphological and immune microenvironment information, while genomic expression data capture pathway activity and resistance mechanisms. Single-modality approaches based on either histopathology or genomics fail to capture complementary tumor information, limiting accurate stratification of responders and non-responders and leading to suboptimal treatment selection. We propose a multimodal fusion network that integrates whole-slide histopathology images and genomic expression data to predict immunotherapy response in NSCLC. Separate encoders process each modality, followed by cross-attention for joint representation learning in an end-to-end framework. The system includes a multiple instance learning-based WSI module, a gene expression encoder with attention over gene sets, and a cross-attention fusion module. The model outputs a binary or probabilistic prediction of treatment response using paired slide and genomic data. The model captures complementary morphological and molecular signals, linking immune infiltration patterns with transcriptomic activity. Attention mechanisms enhance interpretability by highlighting key tissue regions and gene pathways, while also improving robustness to partial modality missingness. This multimodal framework improves NSCLC immunotherapy response prediction by integrating histopathology and genomic data, offering a step toward more precise patient stratification in precision oncology.

Adverse drug reactions (ADRs) are a major global health issue, contributing to significant morbidity, mortality, and healthcare costs. Many ADRs are detected only after widespread drug use, reflecting limitations of pre-market trials in capturing real-world patient variability. Although electronic health records (EHRs) collected between 2017 and 2023 provide rich data for post-market surveillance, they remain underused for systematic ADR detection. Current pharmacovigilance methods rely heavily on spontaneous reporting systems, which suffer from underreporting and bias, while supervised machine learning approaches require labeled ADR data that are often unavailable for rare or novel events. This paper proposes a variational autoencoder (VAE)-based unsupervised framework to detect ADR signals from multimodal EHR data, including clinical notes and laboratory results. The model learns normal patient data distributions and identifies deviations as potential safety signals without requiring labeled ADR examples. A multimodal architecture combines natural language processing of clinical notes with structured laboratory encoders, forming a shared latent space for anomaly detection based on reconstruction error. The framework enables detection of unknown ADRs by flagging abnormal patterns in patient records across large datasets from 2017 to 2023. Its unsupervised nature makes it suitable for identifying rare or previously unrecognized drug safety issues. Overall, this approach offers a scalable, proactive pharmacovigilance strategy that shifts drug safety monitoring from reactive reporting to predictive detection using routine EHR data.

Seasonal influenza and respiratory viruses cause recurring surges in hospital bed demand, often resulting in overcrowding under reactive capacity management. ILI surveillance data and environmental factors such as temperature and humidity provide early signals of transmission, making them valuable for forecasting healthcare demand. Traditional forecasting methods rely on simple time series models or historical averages and fail to capture spatial disease spread or environmental influences, leading to inaccurate predictions and poor resource allocation. We propose a spatiotemporal graph neural network (ST-GNN) that integrates ILI surveillance and environmental data for regional daily hospital bed demand forecasting. The model represents regions as graph nodes connected by population flow, enabling joint spatial-temporal modeling of disease dynamics. The framework uses regional graph construction, ILI data from healthcare visits, and environmental variables such as temperature, humidity, and air quality. These inputs are processed by the ST-GNN to predict daily bed demand. The approach captures spatial disease propagation and improves early detection of demand surges, supporting proactive healthcare planning. The ST-GNN provides a scalable, data-driven framework for improving hospital bed demand forecasting and enhancing preparedness during seasonal epidemics.

Transformer-based architectures have significantly advanced clinical natural language processing by improving the capture of contextual relationships in unstructured electronic health records compared to earlier recurrent and convolutional models, with domain-specific variants such as ClinicalBERT and BioBERT designed to better handle clinical terminology, abbreviations, and specialized language, thereby improving information extraction performance, although the relative impact of different pre-training strategies remains insufficiently synthesized and requires systematic evaluation of corpus selection and fine-tuning approaches; this systematic review mapped studies focusing on pre-training corpora, fine-tuning methods, and named entity recognition performance across entity types such as medications, diseases, procedures, laboratory tests, and social determinants of health, using PRISMA-guided methods and searches across PubMed, ACL Anthology, arXiv, and IEEE Xplore, identifying 32 eligible studies from 1,247 records; findings showed that ClinicalBERT, BioBERT, and PubMedBERT were the most frequently evaluated models, pre-trained on datasets such as MIMIC-III, PubMed abstracts, and mixed biomedical corpora, with consistent evidence that domain-specific pre-training outperforms general-domain BERT models on benchmarks like i2b2 and n2c2 despite variation across entity types and fine-tuning strategies, while clinical pre-training on large EHR corpora improves named entity recognition and optimized fine-tuning approaches such as lower learning rates and data augmentation further enhance performance, particularly for medications and diseases, underscoring the importance of domain adaptation and the need for more standardized evaluation protocols in clinical NLP research.

Patient no-shows in outpatient clinics (5%–30% across specialties) disrupt scheduling efficiency, increase wait times, and strain healthcare resources. To address this, healthcare systems are increasingly applying machine learning (ML) for predictive scheduling support. This systematic review synthesizes ML approaches for predicting outpatient no-shows, focusing on model types, feature usage, and reported operational deployment outcomes, with emphasis on translation into clinical scheduling practice. A PRISMA-compliant search of PubMed, Embase, IEEE Xplore, Scopus, and Web of Science identified studies using ML for no-show prediction in outpatient settings. Data on models, features, performance, and implementation were extracted. Risk of bias was assessed using an adapted PROBAST tool. Thirty-two studies were included. Logistic regression, random forest, and XGBoost were the most commonly used models. Historical attendance data was the dominant predictive feature. Fewer than 20% of studies reported real-world implementation, and reported intervention outcomes (e.g., overbooking, reminders) were inconsistent. While ML models show strong predictive performance, real-world deployment and evidence of operational impact remain limited. This gap highlights the need to prioritize implementation-focused research to translate predictive accuracy into measurable improvements in clinic efficiency and access.

Alzheimer’s disease (AD) is the leading cause of dementia, affecting over 50 million people worldwide, with prevalence expected to triple by 2050. Early detection is crucial for clinical trial enrollment and care planning, and multimodal data (MRI, PET, CSF biomarkers, and cognitive assessments) provides complementary information on neurodegeneration, metabolism, and protein aggregation. This systematic review synthesizes AI/ML approaches for early AD detection using multimodal data, focusing on fusion strategies and performance across disease stages. Following PRISMA guidelines, searches of PubMed, IEEE Xplore, Scopus, Web of Science, and arXiv (2017–2023) identified studies using ML/DL with at least two modalities and reporting diagnostic performance. From 1,247 records, 35 studies were included. MRI was the most used modality (>90%), followed by cognitive tests (70–80%), PET (40–50%), and CSF (20–30%). Early fusion was most common, with increasing use of intermediate fusion. Multimodal models achieved AUROC of 0.90–0.98 for AD vs controls, but lower performance (0.70–0.85) for predicting MCI conversion to AD. Overall, multimodal AI improves early AD detection, with strong performance for diagnosis but persistent challenges in forecasting MCI progression due to heterogeneity and limited longitudinal data.

Hypertension affects about 1.4 billion adults globally and is a major modifiable risk factor for cardiovascular disease. Although several first-line antihypertensive drug classes exist, randomized controlled trials typically report only average treatment effects (ATEs), which mask important variability in individual patient responses. As a result, clinical guidelines often assume a homogeneous patient population, leading to trial-and-error prescribing, delayed blood pressure control, and avoidable adverse effects. I argue that causal forest models combined with double machine learning (DML) enable reliable estimation of heterogeneous treatment effects (HTEs) from observational electronic health record data. These methods can approximate randomized trial validity while capturing clinically meaningful variation in treatment response across patients. Compared with traditional approaches, they are computationally feasible and better suited for individualized treatment assessment. Therefore, comparative effectiveness research in hypertension should move beyond ATE-focused analyses toward routine HTE estimation using causal machine learning. This shift would support more precise, data-driven prescribing and improve patient outcomes.

Rare diseases identified via chest radiography—such as spontaneous pneumothorax, solitary pulmonary nodules, pleural effusions, and cardiomegaly—occur far less frequently than common conditions like pneumonia or chronic obstructive pulmonary disease. Deep learning models require large, balanced datasets for reliable performance, yet rare pathologies remain underrepresented in clinical repositories, limiting real-world deployability. Conventional augmentation methods (geometric and intensity transformations, elastic deformations) add limited variability without creating new pathological patterns. GAN-based approaches can generate synthetic images but often suffer from mode collapse and unrealistic artifacts that reduce lesion fidelity, restricting their effectiveness for rare disease augmentation. We propose a framework based on denoising diffusion probabilistic models (DDPMs) for conditional synthesis of high-fidelity chest X-ray images. The model supports generation conditioned on class labels, segmentation masks, or text prompts, enabling controlled synthesis of rare pathologies and improving dataset balance. The framework includes a forward diffusion process, a U-Net-based reverse denoising model with attention, a multi-modal conditioning mechanism, a lesion-preserving loss function, and an augmentation pipeline combining real and synthetic data. This allows control over lesion type, location, size, and severity, reducing class imbalance and improving classifier performance on rare diseases, as validated through AUC improvements and radiologist assessment. Overall, diffusion-based models provide a scalable and clinically relevant solution for rare disease augmentation in chest radiography, overcoming key limitations of traditional and GAN-based methods and enabling effective use of datasets such as CheXpert, MIMIC-CXR, and ChestX-ray14.

Ovarian cancer, particularly high-grade serous carcinoma, is highly lethal, and accurate survival prediction is essential for treatment planning. However, traditional prognostic models rely on limited clinical and histologic features, while deep learning approaches require expensive pixel-level annotations of whole-slide histopathology images, limiting scalability. We propose a weakly supervised attention-based multiple instance learning (MIL) framework that predicts ovarian cancer survival using only slide-level survival labels. Each whole-slide image is treated as a bag of patches, where a patch encoder extracts features using a pre-trained CNN or vision transformer. An attention-based MIL aggregator assigns importance weights to patches, and a survival head outputs a risk score via a deep Cox model. The attention mechanism enhances interpretability by identifying prognostically relevant regions such as aggressive tumor morphology, stromal patterns, and immune infiltration. This reduces the need for manual annotation while preserving clinical relevance. The framework provides a scalable and interpretable approach for survival prediction and can be evaluated on datasets such as TCGA-OV and CPTAC for clinical translation.

Sepsis prediction models in intensive care units often degrade over time due to changes in clinical practice, patient populations, and data recording processes, a phenomenon known as model drift that can compromise patient safety. Traditional federated learning approaches are not well-suited to these evolving conditions, as they assume static data distributions and typically require costly retraining that risks forgetting previously learned knowledge, while also being constrained by privacy limitations that prevent central data pooling. To address these challenges, this paper proposes a federated continual learning framework that enables ongoing, privacy-preserving model adaptation across multiple hospitals without catastrophic forgetting. The framework integrates local continual learning methods (such as elastic weight consolidation or memory replay) with federated aggregation and importance-weighted parameter updates to support continuous learning from new clinical data while preserving prior knowledge. This design allows each institution to adapt models to local data shifts while collaboratively improving a shared global model without sharing patient-level data. Overall, the proposed approach offers a scalable solution for maintaining robust, adaptive sepsis prediction systems in dynamic healthcare environments, reducing the need for repeated full retraining and supporting long-term clinical deployment.

Neoadjuvant chemotherapy (NAC) is standard for locally advanced breast cancer, with pathologic complete response (pCR) strongly predicting improved survival. However, only 30–40% of patients achieve pCR, while the rest undergo toxicity and delayed surgery without benefit. Current prediction methods rely on tumor volume at isolated time points or simple pre- and post-treatment comparisons, ignoring continuous tumor dynamics during therapy. Sparse and irregular MRI sampling further limits accurate modeling. We introduce a Neural Ordinary Differential Equation (Neural ODE) framework to model continuous tumor growth from sparse serial MRI during NAC. The model learns a time-continuous function describing tumor evolution and predicts individual response trajectories and final pCR status. The framework includes (1) MRI-based tumor segmentation, (2) construction of sparse longitudinal tumor volume series, (3) Neural ODE modeling of continuous dynamics via a neural network–parameterized derivative function, and (4) classification of the final latent state for pCR prediction. An optional module enables trajectory visualization and interpretability. This approach captures hidden continuous tumor behavior between scans, handles irregular sampling without imputation, and enables earlier response prediction. It is also computationally efficient using adjoint-based training and may reveal distinct growth patterns between responders and non-responders. Neural ODE-based modeling offers a more informative framework for predicting NAC response by capturing continuous tumor dynamics, with potential to improve pCR prediction over conventional volume-based methods.

Heart failure affects over 6 million Americans, with 30-day readmission rates remaining 20–25% despite longstanding quality improvement efforts. These readmissions cost about $17 billion annually and are penalized under federal reimbursement programs, yet existing prediction models have not achieved clinically useful performance. Most current models treat patients independently and fail to capture meaningful relationships among patients with similar medication patterns, admission histories, and social circumstances. They also often exclude critical social determinants of health (SDOH), such as housing instability and food insecurity, despite their strong association with readmission risk. In addition, black-box models lack interpretability, limiting clinician trust and usability. I argue that explainable graph neural networks (GNNs) integrating clinical data, SDOH, and prior admissions should replace traditional logistic regression and tree-based models for readmission prediction. Patient similarity graphs can represent clinically relevant relationships that tabular models miss, while graph attention mechanisms provide interpretable, actionable explanations. GNNs enable direct integration of SDOH and prior utilization patterns and offer transparency by highlighting which similar patients most influence predictions. This makes them more suitable for clinical decision support than existing approaches. Overall, persistent readmission rates reflect limitations in current modeling strategies. Explainable GNNs provide a more clinically meaningful and policy-relevant approach to improving prediction and reducing preventable readmissions.

Emergency medical services (EMS) systems face significant challenges in managing fluctuating call volumes, where sudden demand surges can delay response times and worsen outcomes for critical conditions such as cardiac arrest, trauma, and stroke. Accurate forecasting of EMS demand supports proactive ambulance deployment, improving survival rates and resource efficiency, yet traditional methods like ARIMA and exponential smoothing fail to capture nonlinear spatial, temporal, weather, and event-driven effects, and do not provide uncertainty estimates needed for operational decision-making. This paper proposes a conceptual framework based on deep kernel learning with Gaussian processes for spatiotemporal EMS demand forecasting. The model integrates deep neural networks for feature extraction with Gaussian processes for probabilistic inference, enabling both flexible nonlinear representation and uncertainty quantification. It combines temporal, spatial, weather, and event-based kernels to model complex patterns in EMS call volumes. The framework produces predictive mean estimates along with calibrated uncertainty intervals, capturing effects such as weather-driven medical incidents and large public events. This probabilistic output supports risk-aware ambulance allocation strategies that balance over- and under-resourcing. Overall, the proposed approach provides a unified, interpretable, and uncertainty-aware solution for EMS demand forecasting, with future work aimed at validation on real-world datasets and comparison with existing methods.

Prolonged mechanical ventilation (PMV), affecting 5–15% of ICU patients, is associated with high mortality (30–50%), long-term disability, and substantial healthcare costs exceeding $100,000 per admission. These patients often require extended respiratory support beyond 14–21 days and consume significant ICU resources. Current weaning strategies rely on fixed spontaneous breathing trial (SBT) criteria (e.g., RSBI thresholds, oxygenation, respiratory rate), which fail to account for the heterogeneous and evolving physiology of PMV patients. This reduces weaning to discrete events rather than a continuous adaptive process. We propose reinforcement learning from human feedback (RLHF) as a superior framework for weaning, enabling AI systems to learn sequential decision-making policies from clinician preferences across patient trajectories. Traditional protocols ignore temporal dependencies such as prior SBT outcomes, sedation exposure, and respiratory muscle trends. While standard reinforcement learning supports sequential optimization, it depends on difficult-to-define reward functions. RLHF overcomes this by learning reward signals directly from clinician comparisons, aligning model behavior with real-world clinical judgment. Research should shift toward RLHF-based dynamic weaning policies rather than static prediction models. Clinical stakeholders should support data collection and prospective evaluation of RLHF-guided weaning versus standard protocols. RLHF offers a necessary advancement for personalized PMV weaning, addressing limitations of rigid protocols and improving alignment with clinical decision-making.

Chest X-ray report generation is time-consuming and contributes to radiologist workload and burnout, motivating the need for AI systems that can reduce cognitive burden while preserving clinical accuracy. Although encoder-decoder models can generate reports from images, they often suffer from hallucinations, producing findings that are not present or missing real abnormalities due to lack of explicit grounding in evidence, making them unreliable for clinical use. To address this, we propose a cross-modal retrieval framework that generates reports by retrieving and assembling clinically validated sentences from existing radiology reports rather than generating text from scratch. The system uses contrastive learning to align chest X-ray image patches with report sentences in a shared embedding space, enabling retrieval of the most relevant clinical descriptions. A patch encoder extracts visual features, a sentence encoder represents report text, and a retrieval module identifies semantically matching sentences, which are then composed into a coherent final report. Because all outputs are sourced from real clinical reports, the method substantially reduces hallucinations while improving factual reliability and interpretability. This retrieval-based approach offers a scalable and safer alternative to generative models and can be evaluated on datasets such as MIMIC-CXR and CheXpert for clinical accuracy and retrieval performance.