Representation of intensivists' race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models.

Background: Integrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion (DEI) efforts, particularly in visual representations of healthcare professionals. This work aims to examine the demographic representation of two AI text-to-image models, Midjourney and ChatGPT DALL-E 2, and assess their accuracy in depicting the demographic characteristics of intensivists.

A Two-Step Approach Using the National Health Institutes of Health Stroke Scale Assessed by Paramedics to Enhance Prehospital Stroke Detection: A Case Report and Concept Proposal.

Prehospital detection and triage of stroke patients mostly rely on the use of large vessel occlusion prediction scales to decrease onsite time. These quick but simplified scores, though useful, prevent prehospital providers from detecting posterior strokes and isolated symptoms such as limb ataxia or hemianopia. In the present case, an ambulance was dispatched to a 46-year-old man known for ophthalmic migraines and high blood pressure, who presented isolated visual symptoms different from those associated with his usual migraine attacks.

Enhancing Response Rates in Web-Based Surveys: The Impact of Direct Participant Contact.

Achieving a high participation rate is a common challenge in healthcare research based on web-based surveys. A study on local anesthetic systemic toxicity awareness and usage among medical practitioners at two Swiss university hospitals encountered resistance in obtaining personal email addresses from Heads of Departments. Participants were therefore divided into two groups: those who were directly invited via email (personal invitation group) and those who received a generic link through intermediaries (generic link group).