AIMC Topic: Humans

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Integrating AI with tele-ophthalmology in Canada: a review.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
The field of ophthalmology is rapidly advancing, with technological innovations enhancing the diagnosis and management of eye diseases. Tele-ophthalmology, or the use of telemedicine for ophthalmology, has emerged as a promising solution to improve a...

Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.

Journal of cancer education : the official journal of the American Association for Cancer Education
The aim of the present study was to compare the effectiveness of AI-assisted training and conventional human training in clinical practice. This was a multicenter, randomized, controlled clinical trial conducted in five national-level residency train...

Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences.

European radiology
OBJECTIVES: The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or ima...

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Molecular neurobiology
Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the ...

The Potential of Gemini and GPTs for Structured Report Generation based on Free-Text F-FDG PET/CT Breast Cancer Reports.

Academic radiology
RATIONALE AND OBJECTIVE: To compare the performance of large language model (LLM) based Gemini and Generative Pre-trained Transformers (GPTs) in data mining and generating structured reports based on free-text PET/CT reports for breast cancer after u...

Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients.

ESC heart failure
AIMS: This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clini...

Prediction of measles cases in US counties: A machine learning approach.

Vaccine
BACKGROUND: Although measles was declared eliminated from the United States in 2000, the frequency of measles outbreaks has increased in recent years. The ability to predict the locations of future cases could aid efforts to prevent and contain measl...

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) ...

BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction.

Neural networks : the official journal of the International Neural Network Society
Knowledge graph (KG) noise correction aims to select suitable candidates to correct the noises in KGs. Most of the existing studies have limited performance in repairing the noisy triple that contains more than one incorrect entity or relation, which...

Is artificial consciousness achievable? Lessons from the human brain.

Neural networks : the official journal of the International Neural Network Society
We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals se...