AIMC Topic: Humans

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Can machine learning improve on the early prediction of upper limb recovery after stroke?

Journal of neuroengineering and rehabilitation
BACKGROUND: Early prediction of upper limb recovery is important to optimise rehabilitation and inform patients but remains challenging due to inter-individual variability. This study aims to (1) develop and validate a machine learning model to predi...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

Academic misconduct and artificial intelligence use by medical students, interns and PhD students in Ukraine: a cross-sectional study.

BMC medical education
BACKGROUND: The issues regarding the use of artificial intelligence (AI) and academic integrity are important contemporary topics. There are no clear regulations governing the use of AI in academic institutions in Ukraine. This study aimed to explore...

Endodontic microsurgery utilizing an autonomous robotic system for the maxillary second molar: a case report.

BMC oral health
BACKGROUND: Endodontic microsurgery (EMS) is a widely utilized technique for addressing periapical periodontitis that is unresponsive to conventional root canal treatment. Nevertheless, achieving precise root apex location and resection can pose sign...

Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs.

BMC medical education
OBJECTIVE: To evaluate the feasibility and educational value of employing large language models (LLMs) to generate clinical case scenario with multiple-choice questions (MCQs) for undergraduate medical education.

Integrating deep learning and multi-omics features in radiation pneumonitis prediction for lung cancer patients using PET/CT.

BMC medical imaging
BACKGROUND: To investigate the feasibility and accuracy of PET radiomics features, along with their combination with CT radiomics, dosiomics, and deep learning (DL) features, in predicting radiation pneumonitis (RP) in lung cancer patients treated wi...

The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning.

BMC cancer
BACKGROUND: In multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.

Application of artificial intelligence in head and neck tumor segmentation: a comparative systematic review and meta-analysis between PET and PET/CT modalities.

BMC cancer
BACKGROUND: For the effective treatment planning of head and neck cancers, precise tumor segmentation is vital. The combination of artificial intelligence (AI) technology with imaging systems like positron emission tomography (PET) and PET/ computed ...

The effect of kinesiophobia and successful aging on quality of life in older adults: machine learning approach.

BMC geriatrics
BACKGROUND: Kinesiophobia and successful aging are key factors affecting quality of life in older adults; kinesiophobia, the fear of movement, can lead to reduced physical activity, while successful aging promotes overall well-being.

Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity.

BMC cardiovascular disorders
BACKGROUND: Obesity is a disease with high heterogeneity. Both overall obesity and central obesity are associated with increased risks of having cardio-metabolic co-morbidities. This study is aimed to examine the cardio-metabolic characteristics and ...