AI Medical Compendium Journal:
Artificial intelligence in medicine

Showing 81 to 90 of 596 articles

MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question Answering.

Artificial intelligence in medicine
Large Language Models (LLMs) have the potential of facilitating the development of Artificial Intelligence technology to assist medical experts for interactive decision support. This potential has been illustrated by the state-of-the-art performance ...

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis.

Artificial intelligence in medicine
BACKGROUND: Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can ...

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion.

Artificial intelligence in medicine
Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, ...

A Human-AI interaction paradigm and its application to rhinocytology.

Artificial intelligence in medicine
This article explores Human-Centered Artificial Intelligence (HCAI) in medical cytology, with a focus on enhancing the interaction with AI. It presents a Human-AI interaction paradigm that emphasizes explainability and user control of AI systems. It ...

Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method.

Artificial intelligence in medicine
In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple wi...

Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network.

Artificial intelligence in medicine
Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer...

CHNet: A multi-task global-local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer.

Artificial intelligence in medicine
Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatment of advanced colorectal cancer patients. However, despite the excellent performance of deep learning models in certain aspects, they often overlook...

A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease.

Artificial intelligence in medicine
BACKGROUND: Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abilities and impairs a person's capacity for independent functioning. It is a fatal neurodegenerative d...

Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers.

Artificial intelligence in medicine
Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varyi...

Efficient pyramid channel attention network for pathological myopia recognition with pretraining-and-finetuning.

Artificial intelligence in medicine
Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristics of pathology distribution in PM are global-local on the fundus image, which plays a significant role in assisting clinicians in diag...