Preliminary Outcomes 1 Year after Laparoscopic Sleeve Gastrectomy Based on Bariatric Analysis and Reporting Outcome System (BAROS)

# The Author(s) 2011. This article is published with open access at Springerlink.com Background The aim of this study was to assess outcomes of laparoscopic sleeve gastrectomy (LSG) as a stand-alone bariatric operation according to the Bariatric Analysis and Reporting Outcome System (BAROS). Methods Out of 112 patients included and operated on initially, 84 patients (F/M, 63:21) were followed up for 14– 56 months (mean 22±6.75). Patients lost to follow-up did not attend scheduled follow-up visits or they have withdrawn their consent. Mean age was 39 years (range 17–67; SD±12.09) with mean initial BMI 44.62 kg/m 2 (range 29.39–82.8; SD±8.17). Statistical significance was established at the p<0.05 level. Results Mean operative time was 61 min (30–140 min) with mean hospital stay of 1.37 days (0–4; SD±0.77). Excellent global BAROS outcome was achieved in 13 % of patients, very good in 30%, good in 34.5%, fair 9.5 % and failure in 13 % patients 12 months after surgery. Females achieved significantly better outcomes than males with the mean 46.5 % of excess weight loss (EWL) versus 35.3 % of EWL at 12 months (p=0.02). The mean percentage of excess weight loss (%EWL) was 43.6 % at 12 months and 46.6 % at 24 months. Major surgical complication rate was 7.1%; minor surgical complication rate 8.3%. There was one conversion (1.2%) due to the massive bleeding. Comorbidities improved or resolved in numerous patients

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Postglacial migration of silver fir (Abies alba Mill.) to Poland - analysis on the basis of mitochondrial DNA polymorphism

Mitochondrial DNA marker was applied to 10 populations of silver fir from Poland and one from Belarusia. These populations were located within and beyond the natural range of silver fir. The marker exhibited two highly conserved alleles (first – 230 bp and second – 150 bp) based on an insertion−deletion of 80 bp in the fourth intron of the mitochondrial nad5 gene. The geographical distribution of the maternally inherited mitochondrial variation is known to support the existence of at least two refugia with two recolonizing maternal lineages remaining largely separated throughout the range. Our results provide that in all studied populations the first allele was discovered. Therefore we postulate that the silver fir migrate to Poland from the refugium in western Europe (probably from central Italy)