Surveys

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey.

Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin. The original data of this cross-sectional study were obtained from the Nationa...

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack...

ChatGPT in Medical Education: A Precursor for Automation Bias?

Artificial intelligence (AI) in health care has the promise of providing accurate and efficient results. However, AI can also be a black box, where th...

Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.

Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, i...

Artificial Intelligence in endoscopy: A future poll.

Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI syst...

Perceived comfort and tool usability during robot-assisted and traditional laparoscopic surgery: a survey study.

It is known that over half of previously surveyed surgeons performing Robot-Assisted Laparoscopic Surgery (RALS) and three-quarters of those performin...

Artificial Intelligence and Machine Learning May Resolve Health Care Information Overload.

Biomedical information doubles almost every 2 months, and this very rate is expected to double by 2025. The result is information overload for clinici...

Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review.

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP)...

How does artificial intelligence master urological board examinations? A comparative analysis of different Large Language Models' accuracy and reliability in the 2022 In-Service Assessment of the European Board of Urology.

PURPOSE: This study is a comparative analysis of three Large Language Models (LLMs) evaluating their rate of correct answers (RoCA) and the reliabilit...

CanVaxKB: a web-based cancer vaccine knowledgebase.

Cancer vaccines have been increasingly studied and developed to prevent or treat various types of cancers. To systematically survey and analyze differ...

Using skeletal position to estimate human error rates in telemanipulator operators.

In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failu...

[Artificial intelligence and secure use of health data in the KI-FDZ project: anonymization, synthetization, and secure processing of real-world data].

The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection...

Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China).

The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, r...

Challenges of artificial intelligence in medicine and dermatology.

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality....

A machine learning approach to assess Sustainable Development Goals food performances: The Italian case.

In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy's alignment with respect to the Sustainabl...

Responsible use of AI in military systems: prospects and challenges.

Artificial Intelligence (AI) holds great potential for the military domain but is also seen as prone to data bias and lacking transparency and explain...

Clearing the way for participatory data stewardship in artificial intelligence development: a mixed methods approach.

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligenc...

Reliability and validity of artificial intelligence-based innovative digital scale for the assessment of anxiety in children.

AIM: To assess the reliability and validity of an AI-based, innovative digital scale for the assessment of dental anxiety in children.

The first generation of a regional-scale 1-m forest canopy cover dataset using machine learning and google earth engine cloud computing platform: A case study of Arkansas, USA.

Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habita...

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools.

PURPOSE: Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increa...