Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model.

Objectives: To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

Materials And Methods: We identify overlap in the quality management requirements for laboratory testing and clinical AI.

Results: We propose modifications to the CLIA model that could make it suitable for oversight of clinical AI.

Complex portal 2025: predicted human complexes and enhanced visualisation tools for the comparison of orthologous and paralogous complexes.

The Complex Portal (www.ebi.ac.

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study.

Context: Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).

Objectives: Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care.

Methods: We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital.

Noninflammatory 97-amino acid High Mobility Group Box 1 derived polypeptide disrupts and prevents diverse biofilms.

Background: Bacterial biofilm communities are embedded in a protective extracellular matrix comprised of various components, with its' integrity largely owed to a 3-dimensional lattice of extracellular DNA (eDNA) interconnected by Holliday Junction (HJ)-like structures and stabilised by the ubiquitous eubacterial DNABII family of DNA-binding architectural proteins. We recently showed that the host innate immune effector High Mobility Group Box 1 (HMGB1) protein possesses extracellular anti-biofilm activity by destabilising these HJ-like structures, resulting in release of biofilm-resident bacteria into a vulnerable state. Herein, we showed that HMGB1's anti-biofilm activity was completely contained within a contiguous 97 amino acid region that retained DNA-binding activity, called 'mB Box-97'.

Empowering US healthcare delivery organizations: Cultivating a community of practice to harness AI and advance health equity.

Healthcare delivery organizations (HDOs) in the US must contend with the potential for AI to worsen health inequities. But there is no standard set of procedures for HDOs to adopt to navigate these challenges. There is an urgent need for HDOs to present a unified approach to proactively address the potential for AI to worsen health inequities.

Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study.

Background: The problem list (PL) is a repository of diagnoses for patients' medical conditions and health-related issues. Unfortunately, over time, our PLs have become overloaded with duplications, conflicting entries, and no-longer-valid diagnoses. The lack of a standardized structure for review adds to the challenges of clinical use.

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review.

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation.

Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety.

Objectives: Surface the urgent dilemma that healthcare delivery organizations (HDOs) face navigating the US Food and Drug Administration (FDA) final guidance on the use of clinical decision support (CDS) software.

Materials And Methods: We use sepsis as a case study to highlight the patient safety and regulatory compliance tradeoffs that 6129 hospitals in the United States must navigate.

Results: Sepsis CDS remains in broad, routine use.

Patient preferences for treatment attributes in moderate-to-severe atopic dermatitis: a discrete choice experiment.

Evidence on treatment preferences of patients with moderate-to-severe atopic dermatitis (AD) in the United States (US) is limited and an assessment of treatment preferences in this group is warranted. An online discrete choice experiment survey was conducted (June 2023) among US adults with self-reported moderate-to-severe AD or experience with systemic therapy who had inadequate response to topical treatments. Preference weights estimated from conditional logistic regression models were used to calculate willingness to trade off and attributes' relative importance (RI).

Advanced photocatalytic materials based degradation of micropollutants and their use in hydrogen production - a review.

The use of pharmaceuticals, dyes, and pesticides in modern healthcare and agriculture, along with expanding industrialization, heavily contaminates aquatic environments. This leads to severe carcinogenic implications and critical health issues in living organisms. The photocatalytic methods provide an eco-friendly solution to mitigate the energy crisis and environmental pollution.

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare.

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System.

Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics.

Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learning-based approach for accurate skin lesion classification, addressing challenges such as limited data availability, class imbalance, and noise.

Development and Validation of a Natural Language Processing Model to Identify Low-Risk Pulmonary Embolism in Real Time to Facilitate Safe Outpatient Management.

Study Objective: This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously validated risk stratification scores using variables extracted from the electronic health record at the time of diagnosis. The combination of these approaches yielded an natural language processing-based clinical decision support tool that can identify patients presenting to the emergency department (ED) with low-risk PE as candidates for outpatient management.

Methods: Data were curated from all patients who received a PE-protocol computed tomography pulmonary angiogram (PE-CTPA) imaging study in the ED of a 3-hospital academic health system between June 1, 2018 and December 31, 2020 (n=12,183).

Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration.

Objectives: Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children.

Methods: Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS).

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare.

Objective: The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion.

Materials And Methods: Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution.

Does Case-Finding for Admission to Aged Care Rapid Investigation and Assessment Unit for Older Patients Improve Hospital Length of Stay? .

Introduction: Many older people present to emergency departments annually, often with complex geriatric syndromes, yet current acute care models and traditional admissions process may under-serve their needs. The multidisciplinary Aged Care Rapid Investigation and Assessment (ARIA) Unit seeks to bridge this gap, by actively identifying and assessing patients.

Methods: A prospective case-control study was undertaken at a single-centre tertiary referral institution.

Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study.

Background: Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes.

Objective: This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care.

Methods: A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS.

Pond ash as a potential material for sustainable geotechnical applications - a review.

Energy-harnessing sources significantly influence a country's infrastructure and economic development. Though nuclear and hydel power sources are used for energy harnessing, thermal sources are still the primary power source in India and contribute to 75% of the demand. Thermal power plants exploit large volumes of coal reserves.

A framework for understanding label leakage in machine learning for health care.

Introduction: The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule."

Framework: We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions.