AIMC Topic: Humans

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Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers.

Wiener medizinische Wochenschrift (1946)
Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavily rely on various imaging modalities, the introduction of artificial intelligence (AI) into this field has been a logical, inevitable, and successful...

Beyond human perception: challenges in AI interpretability of orangutan artwork.

Primates; journal of primatology
Drawings serve as a profound medium of expression for both humans and apes, offering unique insights into the cognitive and emotional landscapes of the artists, regardless of their species. This study employs artificial intelligence (AI), specificall...

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

La Radiologia medica
The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with th...

End-User Confidence in Artificial Intelligence-Based Predictions Applied to Biomedical Data.

International journal of neural systems
Applications of Artificial Intelligence (AI) are revolutionizing biomedical research and healthcare by offering data-driven predictions that assist in diagnoses. Supervised learning systems are trained on large datasets to predict outcomes for new te...

CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning.

Journal of biomedical informatics
Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical entity rela...

Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.

Chemosphere
Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learning environment. Traditional measurements methods pose challenges in cost, maintenance, and time. This study proposes a new approach using a deep lear...

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review.

Computers in biology and medicine
The increasing complexity and interconnectedness of healthcare data present numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which represents entities and their relationships, is well-suited for modeling ...

Improving explanations for medical X-ray diagnosis combining variational autoencoders and adversarial machine learning.

Computers in biology and medicine
Explainability in Medical Computer Vision is one of the most sensible implementations of Artificial Intelligence nowadays in healthcare. In this work, we propose a novel Deep Learning architecture for eXplainable Artificial Intelligence, specially de...

A novel coarsened graph learning method for scalable single-cell data analysis.

Computers in biology and medicine
The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of cellular heterogeneity, generating vast datasets rich in biological insights. Despite the effectivene...

Prediction and detection of terminal diseases using Internet of Medical Things: A review.

Computers in biology and medicine
The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full po...