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This article analyzes whether Canada's present approach to regulating health-related artificial intelligence (AI) can address relevant safety-related challenges. Focusing primarily on Health Canada's regulation of medical devices with AI, it examines whether the existing regulatory approach can adequately address general safety concerns, as well as those related to algorithmic bias and challenges posed by the intersections of these concerns with privacy and security interests. It identifies several issues and proposes reforms that aim to ensure that Canadians can access beneficial AI while keeping unsafe products off Canadian markets and motivating safe, effective use of AI products for appropriate purposes and populations.

This work presents key features of the engagement of Christian theology, ethics, and related disciplines with the market and economic concerns. It surveys ways that the dialogue has been approached and invites new models and frameworks for the conversation. It contends that economy and Christian thought have long been interconnected, and recounts aspects of this relationship and why it matters for how one might engage the economy ethically and theologically. Finally, it highlights a number of sites of emerging research that are in need of development in light of pressing social, political, economic, and conceptual issues raised by modern life, including money, debt, racial capital, social reproduction, corporations, and cryptocurrency.

The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the promises and challenges of artificial intelligence (AI) in healthcare. Our analysis is directed towards community care practitioners interested in learning more about how AI can change their practice along with the critical considerations required to integrate AI into their practice. We highlight examples of how AI can enable access to new sources of clinical data while augmenting clinical workflows and healthcare delivery. AI can help optimize how and when care is delivered by community practitioners while also improving practice efficiency, accessibility, and the overall quality of care. Unlike virtual care, however, AI is still missing many of the key enablers required to facilitate adoption into the community care landscape and there are challenges we must consider and resolve for AI to successfully improve healthcare delivery. We discuss several critical considerations, including data governance in the clinic setting, healthcare practitioner education, regulation of AI in healthcare, clinician reimbursement, and access to both technology and the internet.

Despite its globalized pretensions to totality, neoliberal capitalism is more fragile and fractured than its exponents or opponents readily admit. Maintenance of fragility is central to contemporary capitalism's operation, and Singh proposes that this behavior may have been learned from Christianity. Engaging in critical examination of the Christian doctrine of the ascension, Singh traces a genealogy of our economic present and finds a resource for Christian protest against it. If the oddity of the ascension, and all its attendant teachings and practices, funds neoliberalized capitalism's denial of vulnerability and claim to supremacy, it may also ground Christian discourse that distends capitalist structures and fosters socio-economic-political alternatives.

Purpose of review Machine learning (ML), a branch of artificial intelligence, is influencing all fields in medicine, with an abundance of work describing its application to adult practice. ML in pediatrics is distinctly unique with clinical, technical, and ethical nuances limiting the direct translation of ML tools developed for adults to pediatric populations. To our knowledge, no work has yet focused on outlining the unique considerations that need to be taken into account when designing and implementing ML in pediatrics. Recent findings The nature of varying developmental stages and the prominence of family-centered care lead to vastly different data-generating processes in pediatrics. Data heterogeneity and a lack of high-quality pediatric databases further complicate ML research. In order to address some of these nuances, we provide a common pipeline for clinicians and computer scientists to use as a foundation for structuring ML projects, and a framework for the translation of a developed model into clinical practice in pediatrics. Throughout these pathways, we also highlight ethical and legal considerations that must be taken into account when working with pediatric populations and data. Summary Here, we describe a comprehensive outline of special considerations required of ML in pediatrics from project ideation to implementation. We hope this review can serve as a high-level guideline for ML scientists and clinicians alike to identify applications in the pediatric setting, generate effective ML solutions, and subsequently deliver them to patients, families, and providers.

Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical note processing; however, broad deployment of ML for this task is restricted by the scarcity and imbalance of labeled training data. In this work we present a method that improves a model’s ability to generalize through novel data augmentation techniques that utilizes information from biomedical ontologies in the form of related medical concepts, as well as global context information within the medical note. We train our model on a public dataset (MIMIC III) and test its performance on automatically generated and hand-labelled datasets from different sources (MIMIC III, CASI, i2b2). Together, these techniques boost the accuracy of abbreviation disambiguation by up to 17% on hand-labeled data, without sacrificing performance on a held-out test set from MIMIC III., Disambiguating abbreviations is important for automated clinical note processing; however, deploying machine learning for this task is restricted by lack of good training data. Here, the authors show novel data augmentation methods that use biomedical ontologies to improve abbreviation disambiguation in many datasets.

Background: Increased patient volumes, wait-times, and long lengths of stay in emergency departments (EDs) are common healthcare challenges associated with poor patient outcomes. We propose a practical machine learning (ML) approach to autonomously ordering downstream testing immediately after patient triage to improve ED efficiency. In doing so, test results can be available to physicians during their initial encounter accelerating clinical decision making. Methods: We created a novel clinical workflow for ED patients through the development of machine learning medical directive (MLMD) models. Logistic regression, random forest, and deep neural network models were trained using retrospective electronic health record (EHR) data collected from the Hospital for Sick Children, a tertiary academic care center in Toronto, Canada. All patients 0-18 years of age presenting to the ED from July 2018 to June 2019 (77,219 total patient visits) were included. MLMD models predict the need for urinary dipstick testing, electrocardiograms, abdominal ultrasounds, testicular ultrasounds, bilirubin testing, and forearm radiographs. Models were optimized to limit the total number of false positives and achieve high positive predictive values (PPVs). Model bias and explainability assessments were also performed to add transparency and trust to our MLMD approach. Findings: MLMD models obtained high area-under-the-receiver-operator curve (0·89-0·99) and PPVs (0·77-0·94). The implementation of MLMD would streamline care for 22·3% of all patient visits, while making test results available earlier by 165 minutes (weighted average) per affected patient. Model explainability assessments demonstrated clinically relevant features having the most impact on predictions with minimal bias present. Interpretation: Our work demonstrates a practical approach for EDs to achieve clinical automation using MLMDs. When integrated into patient workflows, MLMDs can safely and autonomously order common ED tests early in a patient visit with explainability provided to patients and clinicians. Funding: Canadian Institute for Health Research, SickKids Foundation Declaration of Interest: None to declare. Ethical Approval: Data was collected from an EHR system at the Hospital for Sick Children, a tertiary care hospital in the city of Toronto, Canada, for all patients presenting to the ED from July 2018 to June 2019 (77,219 total patient visits) with Research Ethics Board approval.

Supplemental Digital Content is available in the text., Objectives: To design and test a ventilator circuit that can be used for ventilation of two or more patients with a single ventilator, while allowing individualization of tidal volume, fractional concentration of oxygen, and positive end-expiratory pressure to each patient, irrespective of the other patient’s respiratory system mechanics. Design: Description and proof of concept studies. Settings: Respiratory therapy laboratory. Subjects: Ventilation of mechanical test lungs. Interventions: Following a previously advocated design, we used components readily available in our hospital to assemble two “bag-in-a-box” breathing circuits. Each patient circuit consisted of a flexible bag in a rigid container connected via one-way valve to a test lung, along with an inline positive end-expiratory pressure valve, connected to the ventilator’s expiratory limb. Compressed gas fills the bags during “patient” exhalation. During inspiration, gas from the ventilator, in pressure control mode, enters the containers and displaces gas from the bags to the test lungs. We varied tidal volume, “respiratory system” compliance, and positive end-expiratory pressure in one lung and observed the effect on the tidal volume of the other. Measurements and Main Results: We were able to obtain different tidal volume, dynamic driving pressure, and positive end-expiratory pressure in the two lungs under widely different compliances in both lungs. Complete obstruction, or disconnection at the circuit connection to one test lung, had minimal effect (< 5% on average) on the ventilation to the co-ventilated lung. Conclusions: A secondary circuit “bag-in-the-box” system enables individualized ventilation of two lungs overcoming many of the concerns of ventilating more than one patient with a single ventilator.

Increased wait times and long lengths of stay in emergency departments (EDs) are associated with poor patient outcomes. Systems to improve ED efficiency would be useful. Specifically, minimizing the time to diagnosis by developing novel workflows that expedite test ordering can help accelerate clinical decision-making.To explore the use of machine learning-based medical directives (MLMDs) to automate diagnostic testing at triage for patients with common pediatric ED diagnoses.Machine learning models trained on retrospective electronic health record data were evaluated in a decision analytical model study conducted at the ED of the Hospital for Sick Children Toronto, Canada. Data were collected on all patients aged 0 to 18 years presenting to the ED from July 1, 2018, to June 30, 2019 (77 219 total patient visits).Machine learning models were trained to predict the need for urinary dipstick testing, electrocardiogram, abdominal ultrasonography, testicular ultrasonography, bilirubin level testing, and forearm radiographs.Models were evaluated using area under the receiver operator curve, true-positive rate, false-positive rate, and positive predictive values. Model decision thresholds were determined to limit the total number of false-positive results and achieve high positive predictive values. The time difference between patient triage completion and test ordering was assessed for each use of MLMD. Error rates were analyzed to assess model bias. In addition, model explainability was determined using Shapley Additive Explanations values.There was a total of 42 238 boys (54.7%) included in model development; mean (SD) age of the children was 5.4 (4.8) years. Models obtained high area under the receiver operator curve (0.89-0.99) and positive predictive values (0.77-0.94) across each of the use cases. The proposed implementation of MLMDs would streamline care for 22.3% of all patient visits and make test results available earlier by 165 minutes (weighted mean) per affected patient. Model explainability for each MLMD demonstrated clinically relevant features having the most influence on model predictions. Models also performed with minimal to no sex bias.The findings of this study suggest the potential for clinical automation using MLMDs. When integrated into clinical workflows, MLMDs may have the potential to autonomously order common ED tests early in a patient's visit with explainability provided to patients and clinicians.

This article examines expectation, optimism, and future-oriented affective postures in financial markets in conversation with theological debates about hope, fanaticism, and enthusiasm. I identify a tendency in economic discourse to valorize calculative and calm rationality over against emotive enthusiasm in market behavior. In recalling the theological debates over passions and interests that proved influential at the inception of capitalism, I argue that the denigration of exuberance in economics bears genealogical links to theological condemnation of the fanatic and enthusiast in early modernity. Retrieving this theological precursor provides an analytical lens for considering the ongoing traction of the language of rationality in economics.

Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians' information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate's envisioned usage in practice.

It was Franz Overbeck who, in his attack on Harnack, referred to Eusebius's work as “[that] of a hairdresser for the emperor's theological periwig,” and the eminent historian Jacob Burckhardt who declared Eusebius to be “the most objectionable of all eulogists” and “first thoroughly dishonest historian of antiquity.” The summary judgment of such luminaries has aided the tendency to write off the bishop of Caesarea as a hopeless ideologue. In recent decades, a shift has been underway to recalibrate the picture we have of Eusebius, with robust scholarship arguing in support of his work as an historian and biblical scholar. The aim has been in part to distance Eusebius from Constantine, a proximity that is the source of much of the modern consternation with the bishop, given modernity's own genealogical unease with the relation between religion and politics. Whatever Eusebius's actual relations with the emperor, however, his rhetoric of apparently unequivocal exaltation of Constantine endures. Yet this, too, requires reassessment.

This chapter explores the theological–epistemological dynamics of liberation theology. It considers why liberation theology has tended not to express itself within the language of Anglo-American analytic philosophy that characterizes many epistemological discussions. It then provides a description of liberation theology’s own presentation of its epistemological claims, with a special focus on the notion of ‘critical reflection on praxis’. Attention is given to the ways liberation theology insists on the socially located and historically oriented nature of knowledge formation. Finally, the chapter attempts a dialogue between liberation theology and analytically driven categories of epistemology in the interests of mutual clarification and enrichment, and claims that a fruitful conversation can emerge between these disciplines. Each side also presents challenges that the other might consider for its own refinement and development.

Accurate alignment of femoral shaft fractures treated with intramedullary nailing remains a challenge for orthopaedic surgeons. The aim of this study is to develop and validate a cone-beam CT-based, semi-automated algorithm to quantify the malalignment in six degrees of freedom (6DOF) using a surface matching and principal axes-based approach. Complex comminuted diaphyseal fractures were created in nine cadaveric femora and cone-beam CT images were acquired (27 cases total). Scans were cropped and segmented using intensity-based thresholding, producing superior, inferior and comminution volumes. Cylinders were fit to estimate the long axes of the superior and inferior fragments. The angle and distance between the two cylindrical axes were calculated to determine flexion/extension and varus/valgus angulation and medial/lateral and anterior/posterior translations, respectively. Both surfaces were unwrapped about the cylindrical axes. Three methods of matching the unwrapped surface for determination of periaxial rotation were compared based on minimizing the distance between features. The calculated corrections were compared to the input malalignment conditions. All 6DOF were calculated to within current clinical tolerances for all but two cases. This algorithm yielded accurate quantification of malalignment of femoral shaft fractures for fracture gaps up to 60 mm, based on a single CBCT image of the fractured limb.

Though Moltmann and Ricoeur have a history of interaction, little attention has been paid to this relationship and its implications for their respective programmes. These thinkers have much in common, however, and the Ricoeurian categories of surplus and possibility elucidate critical aspects of a theology of hope, serving to strengthen its contemporary implications. Nuance is provided for the resurrection's role in redemption, and an existential mode of hope is delineated. Focusing on Moltmann's interactions with Ricoeur concerning the resurrection elevates these latent themes and demonstrates the fruitfulness of a continued conversation between these two thinkers. Furthermore, examining Moltmann's thought in Ricoeurian perspective opens new directions for conceptualising resurrection hope and praxis in a postmodern context.

The purpose of this work was to compare the absolute breaking strength of various soft tissue skull base (SB) repairs in an in vitro porcine model.A burst pressure (BP) testing system was designed using an axial loading force to create increasing hydraulic pressure. Defects measuring 0.5 × 0.5 cm were created in fascia lata samples. Defects were repaired using various grafts (pericranium and 2 different dural substitutes, Alloderm(®) and Durasis(®)) measuring 1.0 × 1.0 cm to cover the deficient area. Grafts were further reinforced onto the fascia background with either fibrin glue (Tisseel(®)) or hydrogel sealant (DuraSeal™). Each combination of graft and sealant was conducted 6 times and tested 24 hours after the repair.The mean BP (±standard deviation [SD]) were as follows: DuraSeal™-Alloderm, 12.5 ± 5.8 mmHg; DuraSeal™-Durasis, 21.8 ± 20.7 mmHg; DuraSeal™-pericranium, 44.7 ± 30.1 mmHg; Tisseel-Alloderm, 30.6 ± 26.3 mmHg; Tisseel-Durasis, 15.8 ± 18.6 mmHg; and Tisseel-pericranium, 95.5 ± 86 mmHg. One-way analysis of variance showed that the strongest type of repair was Tisseel-pericranium when adjusting for the others (p0.0001). The difference in mean BP of repair with DuraSeal™ vs Tisseel(®) was not statistically significant (p = 0.22). Comparing sealants, the use of Alloderm(®) or Durasis(®) decreased the strength of the repair in comparison to pericranium (p0.0001). Bonferroni analysis showed a significant difference between pericranium and Alloderm(®) (p0.05) and between pericranium and Durasis(®) (p0.05) but not between Alloderm(®) and Durasis(®) (p0.05).In this model, the strongest type of repair (pressure 6 times higher than normal intracranial pressure) was the combination of Tisseel(®)-pericranium. Our data will help guide surgeons who repair SB defects to choose the best graft and sealant.

This paper initiates an investigation into the theological and political dynamics surrounding the nature and function of money. Contrary to views of money’s spontaneous emergence and efficacy, or ideas of its intrinsic worth, I present the understanding of money as an authoritatively instituted sign. Whether emperor, monarch or state, authorizing institutions and discourses are necessary to render money’s performance successful in a given territory. Since in our day this implicates the nation-state, predictions of the state’s demise in light of financial market preponderance appear incoherent. In broaching an analysis of the many elements of money’s function in the nexus of power, politics, and economy, I propose the Christian concept of incarnation as a useful hermeneutic. Incarnational dynamics shed light on attempts at an enforced codification of reality by money’s semiotic institution by the powers, and open up a space for potential critique.

This article explores Agamben's revisionist presentation of the anarchy of the Son and the void of power in the Trinity in his genealogy of economy and government in the West. It argues for a reading that sustains the actual self-depiction of orthodox theology on these points of doctrine in order to evaluate and critique orthodoxy's impact on politics in the West. Only after a thorough assessment of orthodoxy's doctrinal self-understanding can Agamben's reading of potential or suppressed meaning in orthodoxy be appreciated and possibly applied.