AIMC Topic: Humans

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Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene.

Scientific reports
The burden of diet-related diseases is high in Central Asia. In recent years, the field of food computing has gained prominence due to advancements in computer vision (CV) and the increasing use of smartphones and social media. These technologies pro...

Optimizing microfluidic chip for rapid SARS-CoV-2 detection using Taguchi method and artificial neural network PSO.

Scientific reports
Microfluidic biosensors offer a promising solution for real-time analysis of coronaviruses with minimal sample volumes. This study optimizes a biochip for the rapid detection of SARS-CoV-2 using the Taguchi orthogonal table L(3), which comprises nine...

Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination.

Scientific reports
This study aims to compare and evaluate the performance of GPT-3.5, GPT-4, and GPT-4o in the 2020 and 2021 Chinese National Medical Licensing Examination (NMLE), exploring their potential value in medical education and clinical applications. Six hund...

SegMatch: semi-supervised surgical instrument segmentation.

Scientific reports
Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose SegMatch, a semi-supervised learning method to reduce the need for expen...

Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

NPJ primary care respiratory medicine
Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-ma...

Heterogeneous quantization regularizes spiking neural network activity.

Scientific reports
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a ...

A benchmarking framework and dataset for learning to defer in human-AI decision-making.

Scientific data
Learning to Defer (L2D) algorithms improve human-AI collaboration by deferring decisions to human experts when they are likely to be more accurate than the AI model. These can be crucial in high-stakes tasks like fraud detection, where false negative...

Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.

Journal of medical Internet research
BACKGROUND: Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent visi...

Health Care Professionals' Concerns About Medical AI and Psychological Barriers and Strategies for Successful Implementation: Scoping Review.

Journal of medical Internet research
BACKGROUND: The rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction.