AIMC Topic: Humans

Clear Filters Showing 20531 to 20540 of 95995 articles

Supporting Trustworthy AI Through Machine Unlearning.

Science and engineering ethics
Machine unlearning (MU) is often analyzed in terms of how it can facilitate the "right to be forgotten." In this commentary, we show that MU can support the OECD's five principles for trustworthy AI, which are influencing AI development and regulatio...

Emotional Labor and the Problem of Exploitation in Roboticized Care Practices: Enriching the Framework of Care Centred Value Sensitive Design.

Science and engineering ethics
Care ethics has been advanced as a suitable framework for evaluating the ethical significance of assistive robotics. One of the most prominent care ethical contributions to the ethical assessment of assistive robots comes through the work of Aimee Va...

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Human genomics
Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. L...

Machine learning reveals the rules governing the efficacy of mesenchymal stromal cells in septic preclinical models.

Stem cell research & therapy
BACKGROUND: Mesenchymal Stromal Cells (MSCs) are the preferred candidates for therapeutics as they possess multi-directional differentiation potential, exhibit potent immunomodulatory activity, are anti-inflammatory, and can function like antimicrobi...

Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.

BMC medicine
BACKGROUND: Assessing dietary phenylalanine (Phe) tolerance is crucial for managing hyperphenylalaninemia (HPA) in children. However, traditionally, adjusting the diet requires significant time from clinicians and parents. This study aims to investig...

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BMC medicine
BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment...

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

Biomedical physics & engineering express
. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosi...

Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.

Nature genetics
The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, ...

Oscillations in an artificial neural network convert competing inputs into a temporal code.

PLoS computational biology
The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and ...