AIMC Topic: Humans

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Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis.

PloS one
In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by ChatGPT, equ...

Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors.

Molecular diversity
Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into...

When Do Humans Heed AI Agents' Advice? When Should They?

Human factors
OBJECTIVE: We manipulate the presence, skill, and display of artificial intelligence (AI) recommendations in a strategy game to measure their effect on users' performance.

Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders.

Brain pathology (Zurich, Switzerland)
This study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic br...

A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algor...

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma.

European radiology
OBJECTIVES: To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion status in isocitrate dehydrogenase (IDH)-mutant astrocytoma.

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

European radiology
OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep ne...

Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning.

Clinical and experimental medicine
Crohn's disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA sequencing to investigate CD pathogenesis and explore its clinical significance. We identified a dist...

Human's moral judgements towards different social actors: A cross-sectional study.

The British journal of developmental psychology
The proliferation of artificial intelligence may pose new challenges to people's moral judgements. We examined moral judgements towards different social actors and their influencing factors in children, adolescents and adults. Moral judgements were m...

Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.

Heart and vessels
Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accur...