AIMC Topic: Humans

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Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis.

Scientific reports
The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical presentation, laboratory and radiological imaging. However, utilizing such an approach results in as much as 10-15% of negative appendectomies. Hence, ...

Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism.

Nature communications
The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an...

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics.

Nature communications
Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration a...

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

BMJ open diabetes research & care
INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered...

AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare.

Science and engineering ethics
While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI's beneficial outputs and concerns about the challenges of human-computer interaction in healthcare. To address the...

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

Cancer immunology, immunotherapy : CII
BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunother...

DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

BMC medical informatics and decision making
BACKGROUND: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML mode...

Machine learning cryptography methods for IoT in healthcare.

BMC medical informatics and decision making
BACKGROUND: The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this con...

Optimizing motor imagery BCI models with hard trials removal and model refinement.

Biomedical physics & engineering express
Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this p...

Scaling the Idea of Opinion Leadership to Address Health Misinformation: The Case for "Health Communication AI".

Journal of health communication
There is strong evidence of the impact of opinion leaders in health promotion programs. Early work by Burke-Garcia suggests that social media influencers are the opinion leaders of the digital age as they come from the communities they influence, hav...