AIMC Topic: Risk Assessment

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Preferences in AI algorithms: The need for relevant risk attitudes in automated decisions under uncertainties.

Risk analysis : an official publication of the Society for Risk Analysis
Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a pre...

Artificial Intelligence Chatbots' Understanding of the Risks and Benefits of Computed Tomography and Magnetic Resonance Imaging Scenarios.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: Patients may seek online information to better understand medical imaging procedures. The purpose of this study was to assess the accuracy of information provided by 2 popular artificial intelligence (AI) chatbots pertaining to common imagin...

Predicting low cognitive ability at age 5 years using perinatal data and machine learning.

Pediatric research
BACKGROUND: There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for t...

An integrated approach to occupational health risk assessment of manufacturing nanomaterials using Pythagorean Fuzzy AHP and Fuzzy Inference System.

Scientific reports
Nanomaterials (NMs) have the potential to be hazardous owing to their unique physico-chemical properties. Therefore, the need for Health Risk Assessment (HRA) of NMs is expanding. In this study, a novel HRA was developed by the Pythagorean Fuzzy Heal...

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.

Scientific data
Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for ...

Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenoty...

Preoperative Delirium Risk Screening in Patients Undergoing a Cardiac Surgery: Results from the Prospective Observational FINDERI Study.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
OBJECTIVE: Postoperative delirium (POD) is a common complication of cardiac surgery that is associated with higher morbidity, longer hospital stay, cognitive decline, and mortality. Preoperative assessments may help to identify patients´ POD risk. Ho...

Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations aff...

Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches.

Environmental research
Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activiti...