AIMC Topic: Humans

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Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.

Journal of imaging informatics in medicine
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explai...

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.

Journal of imaging informatics in medicine
This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal ...

Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens.

Virchows Archiv : an international journal of pathology
Crohn's disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of ...

Comparing the Quality of Domain-Specific Versus General Language Models for Artificial Intelligence-Generated Differential Diagnoses in PICU Patients.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: Generative language models (LMs) are being evaluated in a variety of tasks in healthcare, but pediatric critical care studies are scant. Our objective was to evaluate the utility of generative LMs in the pediatric critical care setting an...

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

Journal of applied toxicology : JAT
The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evalu...

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.

Medical & biological engineering & computing
Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT...

Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment.

Clinical radiology
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors s...

An artificial intelligence algorithm for co-clustering to help in pharmacovigilance before and during the COVID-19 pandemic.

British journal of clinical pharmacology
AIMS: Monitoring drug safety in real-world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post-marketing scrutiny, in...

Revealing the clinical potential of high-resolution organoids.

Advanced drug delivery reviews
The symbiotic interplay of organoid technology and advanced imaging strategies yields innovative breakthroughs in research and clinical applications. Organoids, intricate three-dimensional cell cultures derived from pluripotent or adult stem/progenit...

Artificial intelligence serving pre-surgical digital implant planning: A scoping review.

Journal of dentistry
OBJECTIVES: To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software.