Scott A. Helgeson

Cardiorenal syndrome (CRS) encompasses a spectrum of disorders arising from bidirectional interactions between the heart and kidneys and is associated with substantial morbidity, mortality, and healthcare burden. Early recognition remains challenging because conventional biomarkers and imaging modalities may inadequately detect subclinical disease or distinguish functional changes from true organ injury. This narrative review examines the epidemiology, classification, pathophysiology, diagnostic approaches, and contemporary management of CRS, with particular emphasis on the emerging role of artificial intelligence (AI). Relevant evidence regarding established and emerging diagnostic and therapeutic approaches was reviewed, including biomarkers, imaging, guideline-directed medical therapy, ultrafiltration, cardiac devices, transplantation, and AI-based applications. Current evidence demonstrates the multifactorial nature of CRS and highlights the importance of early diagnosis and individualized management. Machine learning and deep learning approaches have shown potential to integrate electronic health records, biomarkers, imaging, physiological signals, and multi-omics data to support early detection, risk stratification, outcome prediction, and therapeutic decision-making. AI-assisted approaches may complement conventional diagnostic and management strategies and facilitate more precise and proactive CRS care. However, limitations related to data quality, model interpretability, external validation, and clinical implementation must be addressed before widespread clinical adoption.

Cardiac amyloidosis was long considered rare and untreatable, but both assumptions have given way, and the disease is now recognized as an underdiagnosed and increasingly treatable cause of heart failure. The available therapies work by slowing amyloid deposition, so their benefit depends on early diagnosis, yet the condition is still identified late and usually only after referral for specialized imaging. Artificial intelligence has started to shorten this path. Models built on the electrocardiogram and the echocardiogram detect amyloid infiltration with high accuracy and have reached external validation, while artificial-intelligence-based analysis of heart sound, the cheapest and most widely available cardiac signal, has not yet been directly evaluated in this disease. The physiological rationale for studying it is strong, because a ventricle stiffened by amyloid is expected to alter the low-frequency diastolic sounds that reflect ventricular compliance, although whether these changes are specific to amyloidosis remains unknown. This review brings together the acoustic manifestations of cardiac amyloidosis, the signal-processing and machine learning methods relevant to heart sounds, the clinical evidence across imaging and acoustic modalities, and the practical and equity questions that deployment raises. Heart sound analysis remains the most scalable and least explored opportunity in the field. We outline the mechanistic rationale, the available methods, the lessons from the more mature electrocardiographic and echocardiographic literature, and a set of minimum design requirements for a first disease-specific study.

Objective: To quantify the documented consent outcomes for ambient monitoring technologies in an academic hospital and evaluate whether documented consent acceptance differed by age, gender, and race. Patients and methods: We performed a retrospective cross-sectional study of 9,522 consecutive patients approached for ambient monitoring consent during routine hospital care from 1/1/2025 to 2/12/2026. Consent status was categorized as received, refused, or missing/unable. The primary multivariable analysis used a binary outcome of documented acceptance (received vs. refused or missing/unable) modeled with age, gender, and race. Secondary models used age categories. Results: Overall, 9,125 patients (95.8%) consented, 184 (1.9%) refused, and 213 (2.2%) had missing or unobtainable consent. Acceptance did not differ by gender (95.6% among female patients vs. 96.1% among male patients; adjusted odds ratio [aOR] 1.11, 95% CI 0.91–1.36; p = 0.30). Compared with White patients, lower adjusted odds of acceptance were observed among Black or African American patients (aOR 0.66, 95% CI 0.49–0.88; p = 0.005) and patients categorized as Unknown/Declined/Other (aOR 0.56, 95% CI 0.37–0.83; p = 0.004). Acceptance was highest among Asian patients, but the adjusted difference was not significant (aOR 2.44, 95% CI 0.90–6.59; p = 0.079). Acceptance exceeded 95% across all age groups and was highest among patients aged 65–74 years (96.7%). Younger patients more often refused, whereas older patients more often had missing or unobtainable consent. Conclusion: Documented consent acceptance was high in this single academic hospital. Acceptance did not vary by gender, but modest differences were observed by race and age-related patterns. Consent status alone does not establish patient comprehension, voluntariness, motivation, or durable endorsement. These findings demonstrate operational feasibility while underscoring the need for transparent consent, subgroup monitoring, and prospective evaluation of patient experiences.

Giant cell arteritis (GCA) is the most common primary systemic vasculitis of older adults and has traditionally been framed as an ophthalmologic emergency. Its most serious consequences, however, occur in the cardiovascular system, often years after inflammatory markers have normalized. In contemporary matched population cohorts, patients with GCA carry an approximately two-fold excess risk of aortic aneurysm; the historical Olmsted County cohort reported a 17-fold excess of thoracic aortic aneurysm with wide confidence limits, and the characteristic latency to aneurysm is five to ten years. Patients also face elevated risks of aortic dissection, myocardial infarction, stroke, venous thromboembolism, and heart failure that cluster within the first year of diagnosis, when vascular inflammation is most active. This narrative review, based on a structured search of PubMed/MEDLINE and Google Scholar through September 2026 with independent verification of every cited estimate, synthesizes current evidence on the full spectrum of cardiac and aortic complications of GCA, including aortitis, aneurysm and dissection, coronary arteritis, valvular disease, pericarditis, myocarditis, and conduction disease. We trace their common origin to the immunopathology of the arterial wall, where dendritic cell activation, Th1/Th17 polarization, and maladaptive vascular remodeling produce occlusion in medium-sized arteries and dilation in the aorta. We examine current diagnostic modalities, from the ultrasound-based fast-track clinic, which has reduced permanent visual loss by half or more in observational cohorts, to FDG-PET/CT, in which aortic uptake at diagnosis is associated with subsequent aortic dilation. We review treatment after the GiACTA and SELECT-GCA trials in the light of the 2025 European Alliance of Associations for Rheumatology (EULAR) recommendations, the unresolved role of antiplatelet and statin therapy, and the surgical and endovascular management of GCA-related aortopathy. We suggest that the organization of care, as well as the limitations of drug therapy, may contribute to the residual mortality of GCA, a hypothesis that has not been tested; distinguish recommendations that are supported by current guidelines from those that are our own proposals; and outline a cardio-rheumatology pathway in which baseline vascular phenotyping informs individualized aortic surveillance. GCA is a disease whose natural history often begins in the rheumatology clinic and ends in the cardiac operating room without prior surveillance.

Background: Electrical and dielectric properties of the myocardium depend on its composition, fibrosis, edema, ischemia, and heat damage, and hence may play an important role in defining the electrophysiologic substrate. Local impedance and other impedance-based methods give information about these parameters that is available to clinicians, but not direct or substitutable for intrinsic conductivity and permittivity of the tissue. The use of artificial intelligence has been thoroughly studied for the ECGs, electrograms, images, and computational data analysis, but not for dielectric or impedance measurements. Objective: The study aims to review the scientific basis of dielectric properties of heart muscle critically in cardiology and electrophysiology, to differentiate between existing clinical and conceptual applications of myocardial dielectrics, and to estimate current and potential contributions of artificial intelligence to their interpretation. Data Sources and Study Selection: The current work represents a structured narrative review in PubMed, Scopus, and Google Scholar databases from inception until May 2026 based on various combinations of keywords that refer to myocardial impedance, dielectric properties, conductivity, permittivity, local impedance, dielectric imaging, cardiac electrophysiology, catheter ablation, machine learning, and artificial intelligence. This review included experimental, ex vivo, animal, computational, and human studies related to myocardial dielectric properties and their use in electrophysiology. Evidence was classified according to four categories: direct human clinical evidence; preclinical/translation evidence; indirect evidence from related techniques; and extrapolation concept. As a result of heterogeneity between studies, sensing technologies, and outcomes, the results have been analyzed narratively. Key Findings: Evidence regarding the usage of AI algorithms, which have been primarily trained on myocardial dielectric or catheter-based local impedance data, is extremely scarce and mostly consists of preclinical or ex vivo data. On the contrary, the majority of clinically used AI algorithms in the field of electrophysiology make use of ECG data, intracardiac electrogram data, image processing, or computational modeling without any inputs related to dielectrics or local impedance. Conclusions and Implications for Practice: Dielectric sensing is an attractive supplement to traditional methods of voltage mapping and imaging, although the clinical readiness of dielectric sensing greatly varies depending on the application. At the present time, the existing data provide a basis for utilizing the local impedance measurement in the ablation procedure and evaluation of the myocardium properties, while the combination of artificial intelligence and dielectric sensing is still experimental. Before being introduced into the clinical electrophysiology practice, signal acquisition standardization and prospective multicenter trials are needed.

Objectives: Pulmonary function tests (PFTs) are essential for diagnosis, management, and follow-up of a wide range of respiratory conditions as they provide quantitative insights into various aspects of lung function. Precision in ordering and scheduling PFTs, like any test, is crucial as errors can lead to misdiagnoses, delayed treatments, unnecessary retesting, inflated healthcare costs, and potentially compromised patient care. Hence, the aim of this quality improvement initiative was to reduce the PFT ordering error rate to below 5% by redesigning the ordering workflow and implementing a standardized order set tailored to specific clinical scenarios. Methods: The quality improvement initiative was implemented from August 2023 to March 2024. Data was collected from the electronic medical record (EMR) using predefined operational definitions to identify incorrect PFT orders and scheduling errors. A redesigned orderset was created to align with specific clinical presentations, including symptoms, suspected diagnosis, confirmed diagnosis, and presurgical evaluation. Scheduling was also restructured to match the expected testing time for each category. Education sessions were conducted with ordering providers, and ongoing feedback loops were established. Results: Among 575 preintervention PFT orders, 130 (22.6%) contained errors. Following implementation, review of 607 postintervention orders showed a reduction in error rates to 4.1%, representing an ∼80% improvement ( P <0.01). There was no increase in unnecessary testing, supporting the effectiveness of the intervention without unintended negative consequences. Conclusions: Implementation of a knowledge-based CDSS with standardized order sets significantly reduced PFT ordering errors and variability, improving guideline adherence and care quality. Continued refinement and integration with evolving technologies are essential for broader applicability.

Photoplethysmography (PPG) is a non-invasive optical technique commonly used to measure heart rate and oxygen saturation, but its waveform contains additional physiological information that can be analyzed using artificial intelligence (AI). This narrative review summarizes the emerging applications of AI-based PPG in cardiovascular, respiratory, sleep, hemodynamic, pregnancy-related, and portal-hypertension assessment, with the aim of evaluating its potential beyond conventional monitoring and identifying barriers to clinical translation. The literature search was conducted using PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. Additional relevant studies were identified through screening the reference lists of included articles. Studies published between 2002 and 2026 were identified to capture the development of PPG from conventional monitoring to newer AI-based applications. Human studies were prioritized, while relevant computational, simulated, synthetic, ex vivo, and technical studies were also included. Studies unrelated to PPG, duplicates, and studies with limited relevance were excluded. A total of 96 references were included, covering AI approaches such as convolutional and deep neural networks, ensemble methods, transfer learning, U-Net, generative adversarial networks, and Transformer-based models. Overall, the reviewed evidence suggests that AI-based PPG may support blood pressure estimation, atrial fibrillation detection, sleep and respiratory monitoring, vascular aging assessment, pulmonary hypertension screening, preeclampsia assessment, volume-status and compensatory-reserve assessment, and exploratory assessment related to portal hypertension. However, clinical translation remains limited by motion artifacts, sensor and measurement-site variability, skin-pigmentation-related bias, physiological and environmental influences, heterogeneous methods, limited external validation, and inconsistent clinical and regulatory standards.

Stiff person syndrome (SPS) is a rare autoimmune neurologic disorder that may be accompanied by severe spasms and autonomic instability. We present a man in his 30s with SPS who developed whole-body spasms, severe hypoxemic respiratory failure, and subsequent pulseless ventricular tachycardia/ventricular fibrillation during an attempted extubation at an outside hospital. Initial evaluation demonstrated hyperthermia, shock, ST-segment depression, markedly elevated troponin, and transient severe left ventricular systolic dysfunction; coronary angiography and computed tomography pulmonary angiography excluded obstructive coronary artery disease and pulmonary embolism. After transfer, electrocardiography showed sinus bradycardia with marked QT/QTc (corrected QT interval) prolongation, and while the patient was receiving amiodarone, transesophageal echocardiography showed recovery of left ventricular ejection fraction to 51%, and cardiac magnetic resonance imaging showed patchy mid-wall late gadolinium enhancement with increased T1 and T2 signal compatible with clinically suspected myocarditis. Because endomyocardial biopsy was not performed, the cause of the myocardial injury remained uncertain. Severe hypoxemia, possible sepsis-related myocardial dysfunction, acquired QT prolongation and medication effects, autonomic instability, and cardiac magnetic resonance-supported myocarditis were considered potentially interacting contributors. After recurrent non-sustained ventricular tachycardia and a prior ventricular tachycardia/ventricular fibrillation arrest without a single clearly reversible cause, an implantable cardioverter-defibrillator was placed for secondary prevention. This case illustrates the diagnostic uncertainty surrounding cardiac arrest in SPS and emphasizes the need to evaluate reversible arrhythmic triggers.

Computes predicted values and lower/upper limits of normal for pulmonary function tests according to American Thoracic Society ('ATS') and European Respiratory Society ('ERS') reference standards. Supports spirometry (Global Lung Function Initiative 'GLI' 2012, Quanjer et al. (2012) ; and the race-neutral 'GLI' 2022 / 'GLI Global' equations, Bowerman et al. (2023) ), static lung volumes ('GLI' 2021, Hall et al. (2021) ), and the carbon monoxide transfer factor / diffusion capacity ('GLI' 2017, Stanojevic et al. (2017) , including the 2020 author correction ). Also assigns interpretive pattern labels (Normal, Non-specific, Obstructed, Restricted, Mixed) from spirometry and lung-volume measurements following the 'ERS'/'ATS' 2022 interpretation algorithm, Stanojevic et al. (2022) .

Artificial intelligence (AI) is rapidly transforming healthcare through applications in clinical decision support, diagnostic imaging, population health management, and workflow optimization. Despite these advances, real-world deployment continues to expose critical challenges related to safety, bias, transparency, and integration into clinical workflows. Algorithmic bias can exacerbate health disparities, limited explainability may undermine clinician trust, and insufficient validation and post-deployment monitoring can compromise patient safety. Although the World Health Organization (WHO) has established six ethical principles for AI in health, including autonomy, well-being and safety, transparency, accountability, equity, and sustainability, translating these high-level principles into practical and enforceable governance mechanisms remains a persistent challenge. This narrative review synthesizes insights from bioethics, health policy, computer science, and clinical medicine to identify gaps in current AI governance approaches and proposes a lifecycle-aligned governance-by-design framework that operationalizes WHO ethical principles across key stages of the healthcare AI lifecycle, including data collection, model development, validation, deployment, and post-deployment monitoring. The framework integrates concrete governance mechanisms such as consent governance, fairness evaluation, external validation, explainability, clinician oversight, and continuous performance monitoring. Overall, this work advances a practical, lifecycle-integrated approach to AI governance and provides a structured foundation for developing safe, equitable, and trustworthy AI systems in healthcare.