Preventive Care

Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks

Tuberculosis persists as a global health crisis, especially in resource-limited populations and remote regions, with more than 10 million individuals newly infected annually. It stands as a stark symbol of inequity in public health. Tuberculosis impacts roughly a quarter of the global populace, with the majority of cases concentrated in eight countries, accounting for two-thirds of all tuberculo...

Understanding active learning of molecular docking and its applications

With the advancing capabilities of computational methodologies and resources, ultra-large-scale virtual screening via molecular docking has emerged as a prominent strategy for in silico hit discovery. Given the exhaustive nature of ultra-large-scale virtual screening, active learning methodologies have garnered attention as a means to mitigate computational cost through iterative small-scale doc...

Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers.

Objectives This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms...

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterizatio...

Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer.

Background Retrospective studies have suggested that using artificial intelligence (AI) may decrease the workload of radiologists while preserving mam...

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening f...

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen p...

Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan.

Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials a...

Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway.

Purpose To explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial int...

A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.

Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast canc...

Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depen...

Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.

Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performanc...

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at dif...

[Screening for Characteristic Genes of Different Traditional Chinese Medicine Syndromes of Psoriasis Vulgaris: A Study Based on Bioinformatics and Machine Learning].

OBJECTIVE: To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndro...

Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive care setting.

Our objective was to establish and test a machine learning-based screening process that would be applicable to systematic reviews in pharmaceutical sc...

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complica...

Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.

Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition algorithms are proliferating. Some initial reports claim ...

Human-computer interactions and compassionate healthcare: A Wizard of Oz study using a self-administered AI-assisted cognitive assessment

Artificial intelligence (AI) is increasingly transforming healthcare, however, the interaction between the user, AI, and the user’s environment is poo...

Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing Following an Abnormal Screening Mammogram.

Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aide...

A Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms.

Screening mammogram is a standard and cost-efficient imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arte...