Skip to content

[THE ROLE OF AI TECHNOLOGY IN MAMMOGRAPHIC SCREENING OF BREAST CANCER WITH EVALUATION OF EFFICIENCY AND POTENTIAL FOR PRACTICAL HEALTH CARE].

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.

Read PDF

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

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. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

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. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

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.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

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.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

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. · 121 citations · ⚡9

Related blog posts

Hugging Face Blog Oct 20, 2026

MPS in PyTorch 2.15

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.