Institute of Fundamental Technological Research

The aim of the article is to highlight the key role of artificial intelligence in modern oncology. The search for scientific publications was carried out through the following web search engines: PubMed, PMC, Web of Science, Scopus, Embase and Ebsco. Artificial intelligence plays a special role in oncology and is considered to be the future of oncology. The largest application of artificial intelligence in oncology is in diagnostics (more than 80%),
particularly in radiology and pathology. This can help oncologists not only detect cancer at an early stage but also forecast the possible development of the disease by using predictive models. Artificial intelligence plays a special role in clinical trials. AI makes it possible to
accelerate the discovery and development of new drugs, even if not necessarily successfully. This is done by detecting new molecules. Artificial intelligence enables patient recruitment by combining diverse demographic and medical patient data to match the requirements of a given research protocol. This can be done by reducing population heterogeneity, or by prognostic and predictive enrichment. The effectiveness of artificial intelligence in oncology
depends on the continuous learning of the system based on large amounts of new data but the development of artificial intelligence also requires the resolution of some ethical and legal issues.

artificial intelligence, intelligent oncology, cancer prediction, cancer screening

Aim This study aimed to evaluate the readability and citation practices of artificial intelligence (AI)-generated responses to questions about human metapneumovirus, a respiratory virus of growing public health concern. Subject and methods Five widely used AI chatbots—ChatGPT-4, Copilot, Gemini,Claude.ai, and Grok—were prompted with 14 standardized questions based on official guidelines from the World Health Organization, the Centers for Disease Control and Prevention, and the Australian National Health and Medical Research Council. Responses were anonymized and assessed using six established readability metrics: Flesch–Kincaid Reading Ease and Flesch–Kincaid Grade Level, Gunning Fog Index, SMOG (Simple Measure of Gobbledygook) Index, Coleman–Liau Index, and Automated Readability Index. Scores were compared to standards recommended by the American Medical Association and the National Institutes of Health. Citation frequency and credibility were also analyzed. Results Among 70 chatbot responses, only one met the recommended readability level. Median readability scores ranged from grade 10.4 to 16.0, indicating high complexity. One chatbot generated the most readable content, while another scored lowest. Only two chatbots included source citations. One cited 68 reliable sources, primarily from health organizations and academic institutions, while the other referenced 31 sources of varying quality. Conclusion AI-generated health content often exceeds recommended readability thresholds and lacks consistent citation practices. These issues may hinder understanding and trust. Improving default readability settings and integrating real-time citation features could enhance the accessibility and credibility of chatbot-based health communication.

Human metapneumoviru, Artificial intelligence, Chatbots , Readability

Background: Chatbots using the Large Language Model (LLM) generate human responses to questions from all
categories. Due to staff shortages in healthcare systems, patients waiting for an appointment increasingly use
chatbots to get information about their condition. Given the number of chatbots currently available, assessing the
responses they generate is essential.
Methods: Five chatbots with free access were selected (Gemini, Microsoft Copilot, PiAI, ChatGPT, ChatSpot) and
blinded using letters (A, B, C, D, E). Each chatbot was asked questions about cardiology, oncology, and psoriasis.
Responses were compared to guidelines from the European Society of Cardiology, American Academy of
Dermatology and American Society of Clinical Oncology. All answers were assessed using readability scales
(Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level and Dale-Chall Score). Using a 3-
point Likert scale, two independent medical professionals assessed the compliance of the responses with the
guidelines.
Results: A total of 45 questions were asked of all chatbots. Chatbot C gave the shortest answers, 7.0 (6.0 – 8.0), and Chatbot A the longest 17.5 (13.0 – 24.5). The Flesch Reading Ease Scale ranged from 16.3 (12.2 – 21.9)
(Chatbot D) to 39.8 (29.0 – 50.4) (Chatbot A). Flesch-Kincaid Grade Level ranged from 12.5 (10.6 – 14.6) (Chatbot A) to 15.9 (15.1 – 17.1) (Chatbot D). Gunning Fog Scale Level ranged from 15.77 (Chatbot A) to 19.73 (Chatbot D). Dale-Chall Score ranged from 10.3 (9.3 – 11.3) (Chatbot A) to 11.9 (11.5 – 12.4) (Chatbot D).
Conclusion: This study indicates that chatbots vary in length, quality, and readability. They answer each question
in their own way, based on the data they have pulled from the web. Reliability of the responses generated by
chatbots is high. This suggests that people who want information from a chatbot need to be careful and verify the answers they receive, particularly when they ask about medical and health aspects.

Chatbots,Readability,Cardiovascular health,Oncology

Background. The coronavirus disease 2019 (COVID-19) pandemic has significantly accelerated the development and use of new healthcare technologies. While younger individuals may have been able to quickly embrace virtual advancements, older adults may still have different needs in terms of health communication.

Objectives. To identify areas of interest and preferred sources of information related to the COVID-19 pandemic among older adults and to verify their eHealth competencies.

Materials and methods. The study was conducted between February 2022 and July 2022. It included listeners from the University of the Third Age (U3A) and younger students. Both groups received information about the HealthBuddy+ chatbot, a questionnaire that addressed respondents’ interests about COVID-19, and the PL-eHEALS (eHealth Literacy Scale) questionnaire to measure their eHealth competencies.

Results. There were 573 participants in the study (U3A listeners – 303 participants, median age: 73 years (interquartile range (IQR): 69–77); young adult students – 270, median age: 24 years (IQR: 23–24). The primary source of information about COVID-19 for older adults was television (84.5%), and for younger adults, internet (84.4%). Among the older adults, only 17% ever interacted with a chatbot (younger adults – 78% respectively), and 19% considered it a trustworthy source of information on COVID-19 compared to 79% of younger respondents. Older adults and younger adults in our study were most interested in COVID-19 treatment methods (45.5% and 69.3%, respectively), symptoms of the disease (36.6% and 35.2%, respectively) and chronic diseases coexisting with COVID-19 (35.0% and 51.5%, respectively). However, their eHealth competencies were generally low (median (Me): 34; IQR: 30–39) compared to younger adults (Me: 42; IQR: 40–47).

Conclusions. Health education for older adults should be appropriately tailored to their current needs and differentiated. The level of eHealth competencies of older adults suggests that much work remains to narrow the gap between the eHealth competencies of the younger and older generations.

health education,older adults,information seeking,COVID-19

Abstract: BackgroundDiet-related diseases remain leading causes of death in most developed countries around the world. The aim of the study was to compare opinions of patients and family physicians on receiving and providing recommendations about physical activity, diet and use of medication. Methods: The questionnaire study was conducted among patients of 36 primary health care clinics in Poland between September 2018 and February 2019. Patients and physicians were interviewed separately by trained researchers. Data from 509 patients and 167 family doctors were analyzed. Results: The median age of patients was 44 years (interquartile range: 29–55) and 70% were women. The majority of physicians were women (59%) and the median age was 37 years (IQR: 31–50). There was a significant difference between physicians’ declarations on providing recommendations on diet (92% vs. 39%) and activity (90% vs. 37%) versus patients’ declarations on receiving them. Conclusions: The results indicate that there is significant room for improvement in providing patients with proper recommendations on diet and physical activity by their family physicians. Primary care physicians should put more emphasis on clear communication of recommendations on diet and physical activity.

diet

Introduction. To investigate the public interest in cancer screening before, during and after one year of the COVID-19 pandemic, in relation to the number of cases and deaths caused by the coronavirus. Material and methods. Google Trends (GT) was used to obtain data on online interest in screening for the most common cancer types during COVID-19 pandemic. Results. It was found that although online interest in screening collapsed during the early stages of the pandemic, it managed to gradually return to its pre-pandemic levels six months later despite a growing number of COVID-19 related deaths. Nevertheless, some data and reports suggest that this unprecedented crisis may result in increased mortality and incidence rates. Conclusions. The study raises the importance of continuous and active actions aimed at raising cancer awareness which appears to be crucially important during a public health crisis such as the COVID-19 pandemic.

cancer screening

The aim of this study was to analyse knowledge on colon cancer prevention among patients of primary care and identify their sources of information. The questionnaire study was conducted among patients of 36 primary healthcare clinics in Poland between September 2018 and February 2019. Patients were interviewed separately by trained researchers. Over 39% of the primary health patients declared that their knowledge about colon cancer prevention is unsatisfactory. Information about colon cancer prevention varied according to sex, age and BMI. Men declared lower level of knowledge than women: 46% of men thought it was unsatisfactory compared with 36%of women (p = 0.003). Preventive recommendations weremore often provided to patients over 60 years old (p < 0.01). Overweight and obese patients were more likely to receive recommendations on diet (p < 0.001) and physical activity (p < 0.001) than patients with normal weight. Themost common source of information on colon cancer prevention was Internet (68%) and medical doctors (60%). There is a need for developing colon cancer prevention policy. Crucial aspect includes educational programs aimed at improving patient’s knowledge and involving medical staff. The policymakers should pay greater attention to cancer prevention policies and medical staff involved in prevention to quality of communication to make sure patients thoroughly understand information they are provided.

colon cancer, prevention, primary healthcare, education, communication