Dec 2026· Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny· Vol 34 4, pp.
851-856
· 0 citations· 4 references
Medicine
Abstract
In conditions of increasing workload on organized mammographic screening programs and deficiency of qualified personnel, implementation of auxiliary technologies on the basis of artificial intelligence (AI) acquires special actuality. The results of retrospective cohort study included 100 histologically verified cases of breast cancer (BI-RADS category 6) permitted to evaluate diagnostic efficiency of AI algorithm as compared to independent conclusions of three certified radiologists with length of service more than 10 years. The sensitivity of technology of AI amounted 86%-96%, depending on dichotomization model. The percentage of total coincidence with appraisal of experts varied within the limits 87-91%. The level of concordance, assessed using weighted Cohen's kappa coefficient, came up to κq = 0.76 (95% CI: 0.68-0.83) that corresponds to "good" concordance. In 1%-3% of cases, the AI algorithm identified suspicious findings missed by experts, at that without increasing volume of groundless examinations due to conservative classification of most disputable cases as BI-RADS 3 with ordering dynamic observation. The obtained results confirm clinical applicability of AI technology as standardizing and relieving tool in practical health care, capable to increase sensitivity of screening and to decrease risk of false-negative conclusions without violation of established clinical protocols.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9