AIMC Topic: Humans

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User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.

JMIR cancer
BACKGROUND: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial ...

The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.

JMIR medical informatics
BACKGROUND: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients'...

Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI).

Langenbeck's archives of surgery
PURPOSE: This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.

Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.

Brain topography
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, a...

Scalable information extraction from free text electronic health records using large language models.

BMC medical research methodology
BACKGROUND: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting...

Development of a Preliminary Patient Safety Classification System for Generative AI.

BMJ quality & safety
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classificat...

Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients.

BMJ quality & safety
BACKGROUND: Search engines often serve as a primary resource for patients to obtain drug information. However, the search engine market is rapidly changing due to the introduction of artificial intelligence (AI)-powered chatbots. The consequences for...

Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs.

IEEE journal of translational engineering in health and medicine
The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through struct...

Identification of WDR74 and TNFRSF12A as biomarkers for early osteoarthritis using machine learning and immunohistochemistry.

Frontiers in immunology
BACKGROUND: Osteoarthritis (OA) is a chronic joint condition that causes pain, limited mobility, and reduced quality of life, posing a threat to healthy aging. Early detection is crucial for improving prognosis. Recent research has focused on the rol...

Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement.

NeuroImage. Clinical
Deep learning-based tractography implicitly learns anatomical prior knowledge that is required to resolve ambiguities inherent in traditional streamline tractography. TractSeg is a particularly widely used example of such an approach. Even though it ...