AIMC Topic: Artificial Intelligence

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AI-driven fusion of multimodal data for Alzheimer's disease biomarker assessment.

Nature communications
Alzheimer's disease (AD) diagnosis hinges on detecting amyloid beta (Aβ) plaques and neurofibrillary tau (τ) tangles, typically assessed using PET imaging. While accurate, these modalities are expensive and not widely accessible, limiting their utili...

Evaluating a Customized Version of ChatGPT for Systematic Review Data Extraction in Health Research: Development and Usability Study.

JMIR formative research
BACKGROUND: Systematic reviews are essential for synthesizing research in health sciences; however, they are resource-intensive and prone to human error. The data extraction phase, in which key details of studies are identified and recorded in a syst...

Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm.

Scientific reports
Weeds and crops contribute to a endless resistance for similar assets, which leads to potential declines in crop production and enlarged agricultural expenses. Conventional models of weed control like extensive pesticide use, appear with the hassle o...

AI simulation models for diagnosing disabilities in smart electrical prosthetics using bipolar fuzzy decision making based on choquet integral.

Scientific reports
The integration of AI simulation models within smart electrical prosthetic systems represents a significant advancement in disability disease diagnosis. However, the selection and evaluation of these AI models interpret some multi-criteria decision-m...

Large-scale transformer-based topic graphs identify thematic links between engineering and biology.

Scientific reports
We develop an AI system that pairs engineering problems with biology-inspired solutions at a large scale, by analyzing over 101 million abstracts to identify thematic links between engineering and biology. We detect coherent themes in each domain wit...

Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images.

Scientific reports
Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood ves...

Human-alignment influences the utility of AI-assisted decision making.

Scientific reports
Whenever an AI model is used to predict a relevant (binary) outcome in AI-assisted decision making, it is widely agreed that, together with each prediction, the model should provide an AI confidence value. However, it has been unclear why decision ma...

Developing an AI-powered wound assessment tool: a methodological approach to data collection and model optimization.

BMC medical informatics and decision making
BACKGROUND: Chronic wounds (CWs) represent a significant and growing challenge in healthcare due to their prolonged healing times, complex management, and associated costs. Inadequate wound assessment by healthcare professionals (HCPs), often due to ...

Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours.

Scientific reports
This study uses artificial intelligence (AI) for differentiation between salivary gland tumours (SGT) using digitised Haematoxylin and Eosin stained whole-slide images (WSI). Machine learning (ML) classifiers were developed and tested using 320 scann...

Reframing robotics in Mohs surgery for rare cutaneous sarcomas: conceptual promise and clinical realities in precision oncology.

Journal of robotic surgery
Dermatofibrosarcoma protuberans (DFSP) is a rare, locally aggressive cutaneous sarcoma in which achieving histologically negative margins is paramount to minimizing recurrence. Mohs micrographic surgery (MMS) has transformed margin control in DFSP by...