Sex and age differences and outcomes in acute coronary syndromes

Background: There is conflicting information about sex differences in presentation, treatment, and outcome after acute coronary syndromes (ACS) in the era of reperfusion therapy and percutaneous coronary intervention. The aim of this study was to examine presentation, acute therapy, and outcomes of men and women with ACS with special emphasis on their relationship with younger age ( lt = 65 years). Methods: From January 2010 to June 2015, we enrolled 5140 patients from 3 primary PCI capable hospitals. Patients were registered according to the International Survey of Acute Coronary Syndrome in Transitional Countries (ISACS-TC) registry protocol (ClinicalTrials.gov: NCT01218776). The primary outcome was the incidence of in-hospital mortality. Results: The study population was constituted by 2876 patients younger than 65 years and 2294 patients older. Women were older than men in both the young (56.2 +/- 6.6 vs. 54.1 +/- 7.4) and old (74.9 +/- 6.4 vs. 73.6 +/- 6.0) age groups. There were 3421 (66.2%) patients with ST elevation ACS (STE-ACS) and 1719 (33.8%) patients without ST elevation ACS (NSTE-ACS). In STE-ACS, the percentage of patients who failed to receive reperfusion was higher in women than in men either in the young (21.7% vs. 15.8%) than in the elderly (35.2% vs. 29.6%). There was a significant higher mortality in women in the younger age group (age-adjusted OR 1.52, 95% CI: 1.01-2.29), but there was no sex difference in the older group (age-adjusted OR 1.10, 95% CI: 0.87-1.41). Significantly sex differences in mortality were not seen in NSTE-ACS patients. Conclusions: In-hospital mortality from ACS is not different between older men and women. A higher short-term mortality can be seen only in women with STEMI and age of 65 or less

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic