Phantoms are used to validate diagnostic imaging methods or improve the skills of medical professionals. For instance, they allow conducting an unlimited number of imaging studies during medical training, assessing image quality, optimizing radiation dose, and testing novel techniques and equipment. Researchers in breast imaging use anthropomorphic models to validate, assess, and optimize new methods for diagnosing breast diseases. Such models also facilitate control over the quality of diagnostic systems, help optimize clinical protocols, and improve image reconstruction algorithms. Realistic simulation of the breast tissue is essential to address the challenges of advancing X-ray mammary gland studies. The review aimed to describe phantoms currently available for diagnostic imaging and the way they were fabricated. In this literature review, PubMed, eLIBRARY, and Google Scholar databases were screened for relevant articles. Thus, 72 articles and 13 conference papers were included. The study two major types of breast phantoms: computational and physical. Specifically, computational phantoms are classified into subgroups depending on the data they use. These include mathematical models, tissue samples, and medical images of the breast. The classification of the physical phantoms is based on their manufacturing process: casting silicone-like substances, 3D printing with resins and plastics, or printing on paper using X-ray contrast ink. Computational phantoms are generally advantageous with respect to versatility, efficiency, precision, and safety and allow the generation of large amounts of virtual data. Physical phantoms provide the most realistic diagnostic images without the need for a patient and allow performing an unlimited number of radiological studies.

Introduction: Artificial Intelligence (AI) is becoming an essential part of modern radiology. However, available evidence highlights issues in the real-world applicability of AI tools and mixed radiologists’ acceptance. We aimed to develop and validate a questionnaire to evaluate the attitude of radiologists toward radiology AI (ATRAI-14). Materials and Methods: We generated items based on the European Society of Radiology questionnaire. Item reduction yielded 23 items, 12 of which contribute to scoring. The items were allocated into four domains (“Familiarity”, “Trust”, “Implementation Perspective”, and “Hopes and Fears”) and a part related to the respondent’s demographics and professional background. As a pre-test method, we conducted cognitive interviews with 20 radiologists. Pilot testing with reliability and validity assessment was carried out on a representative sample of 90 respondents. Construct validity was assessed via confirmatory factor analysis (CFA). Results: CFA confirmed the feasibility of four domains structure. ATRAI-14 demonstrated acceptable internal consistency (Cronbach’s Alpha 0.78 95%CI [0.68, 0.83]), good test–retest reliability (ICC = 0.89, 95% CI [0.67, 0.96], p-value < 0.05), and acceptable criterion validity (Spearman’s rho 0.73, p-value < 0.001). Conclusions: The questionnaire is useful for providing detailed AI acceptance measurements for making management decisions when implementing AI in radiology.

Background/Objectives: Large language models (LLMs) are increasingly considered for use in radiology, including the summarization of patient medical records to support radiologists in processing large volumes of data under time constraints. This task requires not only accuracy and completeness but also clinical applicability. Automatic metrics and general-purpose questionnaires fail to capture these dimensions, and no standardized tool currently exists for the expert evaluation of LLM-generated summaries in radiology. Here, we aimed to develop and validate such a tool. Methods: Items for the questionnaire were formulated and refined through focus group testing with radiologists. Validation was performed on 132 LLM-generated summaries of 44 patient records, each independently assessed by radiologists. Criterion validity was evaluated through known-group differentiation and construct validity through confirmatory factor analysis. Results: The resulting seven-item instrument, ELEGANCE (Expert-Led Evaluation of Generative AI Competence and Excellence), demonstrated excellent internal consistency (Cronbach’s α = 0.95). It encompasses seven dimensions: relevance, completeness, applicability, falsification, satisfaction, structure, and correctness of language and terminology. Confirmatory factor analysis supported a two-factor structure (content and form), with strong fit indices (RMSEA = 0.079, CFI = 0.989, TLI = 0.982, SRMR = 0.029). Criterion validity was confirmed by significant between-group differences (p < 0.001). Conclusions: ELEGANCE is the first validated tool for expert evaluation of LLM-generated medical record summaries for radiologists, providing a standardized framework to ensure quality and clinical utility.

Background: Inflammatory bowel diseases (IBD), associated with a significant burden on patients’ lives, are becoming increasingly common. Patients with IBD need continuous treatment and lifelong monitoring, which could be achieved by telemonitoring. Telemonitoring has been shown to be effective in improving outcomes for patients with IBD, and can provide a more convenient and accessible way for patients to receive care. However, the certainty of evidence remains low. This article outlines the methodology of a randomized control study that aims to assess the efficacy of telemonitoring compared to face-to-face follow-up for patients with IBD in Russia, hypothesizing that the implementation of telemonitoring will lead to improvement in clinical, social, and organizational areas. Methods: The TIGE-Rus study is a randomized controlled trial. The study consists of three stages, including selection of patients and random assignment into two groups with a ratio of 1:1, follow-up care using telemonitoring or face-to-face appointments, and evaluation and comparison of follow-up efficacy in both groups. In the first stage, all patients will undergo laboratory tests and instrumental examinations, and fill out questionnaires to measure disease activity, quality of life, medication adherence, psychological well-being, and satisfaction with medical care. In the second stage, the control group will receive standard care while the telemonitoring group will have access to a web platform where they can report their clinical activity, fill out questionnaires, and have online consultations with gastroenterologists. The gastroenterologists will also make monthly phone calls to each patient in the telemonitoring group to monitor their progress. In the third stage of the study, both the telemonitoring group and the control group will be re-hospitalized after six months of monitoring. IBD activity will be evaluated through laboratory and instrumental examinations. Additionally, all the participants will complete questionnaires to assess the disease activity, medication adherence, quality of life, psychological well-being, and satisfaction with medical care in both groups. Conclusions: The trial will explore whether telemonitoring is effective in improving clinical, social, and organizational aspects in the management of patients with IBD in the setting of the Russian healthcare system.

Large generative models (LGMs) have significant potential for healthcare and medical science. While publications are growing exponentially, LGM studies lack quality and breakthrough findings. Research articles call for standardized approaches to ensure safe and effective integration of LGMs into clinical practice. Currently, the Moscow healthcare system is testing LGMs as tools for supporting medical decision-making, which has required development of specialized methods and techniques for assessing LGM quality. This paper presents two methods for assessing the quality of large generative models. Both methods are based on analysis of literature data (over 200 sources), results from comprehensive testing of 204 LGMs, and hands-on experience in assessing model quality using a sample of more than 12,000 cases. Designed for two main LGM application scenarios, the methods incorporate a dedicated approach to building test samples, tailored and validated questionnaires, testing methodologies, and unified requirements for the composition and structure of quality assessment outputs.

BACKGROUND: Patients with inflammatory bowel disease often require lifelong follow-up by a clinician. Telemedicine monitoring is a promising area of such healthcare services, often based on the evaluation of patients’ remote questionnaire results by a medical practitioner. AIM: To define, using the Delphi method, a list of questionnaire-assessed parameters for monitoring and treating patients with IBD. MATERIALS AND METHODS: The study was conducted in three stages. An electronic survey form was created to collect information, ensuring that the respondent’s experience was included when completing the survey. In the first stage, respondents answered an open-ended question about what parameters assessed by questionnaires should be monitored in patients with IBD. In the second stage, participants answered the same question but selected any number of items from a list. In the third stage, the responses were analyzed. The primary endpoint was a consensus on each parameter, defined as 75% respondent agreement. RESULTS: The study had 15 participants, 13.3% of whom were male. Of all respondents, 46% worked in an outpatient setting, whereas 54% worked in an inpatient setting. Their ages ranged from 25 to 53 years, with 53% of the participants having 1–4 years of experience and 47% having 17–29 years of experience. None of the parameters reached a 75% agreement level based on the results of the first stage. In the second stage, respondents reached a consensus on 72% of the parameters. No relationship was found between respondents’ age, sex, years of experience, or job settings and responses in the first and second stages. CONCLUSIONS: The final list of parameters recommended for evaluation during the monitoring and treatment of patients with IBD included abdominal pain, frequency of defecation and stool quality, presence of pathological stool impurities, body temperature, joint/muscle pain, sleep quality, anxiety, depression, work capacity for employed/ability to attend lessons for students, energy and quantity of vigor, fixation on the disease, patients’ general evaluation of their quality of life, and treatment adherence.