- MeSH
- Emulsions MeSH
- Expert Systems MeSH
- Technology, Pharmaceutical MeSH
- Artificial Intelligence MeSH
- Publication type
- Comparative Study MeSH
Zpracování znalostí zatížených nejistotou je jednou z nejdůležitějších aplikací metod umělé inteligence. Použití technologie bayesovských sítí umožňuje pro tyto ucely využít výsledky po několik století budované teorie pravděpodobnosti a pracovat s mnohorozměrnými pravdepodobnostními distribucemi V tomto případě muže být rozměr distribucí roven stovkám, případně i tisícům. To znamená, že tato technologie může být použita na reálné aplikace, na skutečné problémy, jejichž složitost přesahuje možnosti většiny dalších přístupů pro modelování nejistých znalostí. Vzhledem k tomu, že se jedná o poměrně mladou disciplínu, nelze říci, že všechny teoretické problémy a problémy spojené s návrhem aplikací již byly úspěšně vyřešeny. Nejvíce otevřených problémů je spojeno právě s konstrukcí bayesovských sítu Přesto sejižobjevují aplikace, které naznačují, že bayesovské sítě se stanoujednítn z mocných nástrojů umělé inteligence pro řešení složitých problémů. Proto lze předpokládat, že se s bayesovskými sítěmi budeme v blízké budoucnosti setkávat i v medicíně, která je jednou z oblastí, kde deterministická znalost je spíše výjimkou.
Uncertain knowledge processing is one of the most important applications of artificial intelligence. Bayesian network technology, taking advantage of for several centuries developed results of probability theory, enables processing of multidimensional probability distributions whose dimensionality equals hundreds or even thousands. Therefore, this technology can be applied to real-life problems whose complexity goes beyond cambility of most other approaches for uncertain knowledge processing. It cannot be said that this relatively new discipline has Iready solved all its theoretical and practical problems. Most of still open problems are connected with zonstraction of Bayesian network models for practical applications. Nevertheless, recently published applications suggest that Bayesian network will become one of he most powerful tool of artificial intelligence for uncertain knowledge processing. Therefore, we can assume that in near future we shall meet Bayesian network in medical applications as this field is one of those where deterministic knowledge is exception.
- Keywords
- Hackathon,
- MeSH
- Digital Technology * MeSH
- Humans MeSH
- Interdisciplinary Communication MeSH
- Delivery of Health Care * MeSH
- Software MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- News MeSH
- MeSH
- Diagnostic Errors legislation & jurisprudence MeSH
- Diagnostic Techniques and Procedures trends MeSH
- Medical Informatics * legislation & jurisprudence MeSH
- Humans MeSH
- Patient Harm legislation & jurisprudence MeSH
- Liability, Legal MeSH
- Artificial Intelligence * legislation & jurisprudence MeSH
- Check Tag
- Humans MeSH
The quandary known as the Intracytoplasmic Sperm Injection (ICSI) paradox is found at the juncture of Assisted Reproductive Technology (ART) and 'andrological ignorance' - a term coined to denote the undervalued treatment and comprehension of male infertility. The prevalent use of ICSI as a solution for severe male infertility, despite its potential to propagate genetically defective sperm, consequently posing a threat to progeny health, illuminates this paradox. We posit that the meteoric rise in Industrial Revolution 4.0 (IR 4.0) and Artificial Intelligence (AI) technologies holds the potential for a transformative shift in addressing male infertility, specifically by mitigating the limitations engendered by 'andrological ignorance.' We advocate for the urgent need to transcend andrological ignorance, envisaging AI as a cornerstone in the precise diagnosis and treatment of the root causes of male infertility. This approach also incorporates the identification of potential genetic defects in descendants, the establishment of knowledge platforms dedicated to male reproductive health, and the optimization of therapeutic outcomes. Our hypothesis suggests that the assimilation of AI could streamline ICSI implementation, leading to an overall enhancement in the realm of male fertility treatments. However, it is essential to conduct further investigations to substantiate the efficacy of AI applications in a clinical setting. This article emphasizes the significance of harnessing AI technologies to optimize patient outcomes in the fast-paced domain of reproductive medicine, thereby fostering the well-being of upcoming generations.
Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.
- MeSH
- Deep Learning MeSH
- Gastroenterology * MeSH
- Humans MeSH
- Artificial Intelligence * MeSH
- Natural Language Processing * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- MeSH
- Medical Informatics * ethics methods trends MeSH
- Humans MeSH
- Personal Autonomy MeSH
- Risk MeSH
- Robotics ethics trends MeSH
- Mental Health Services ethics legislation & jurisprudence MeSH
- Privacy legislation & jurisprudence MeSH
- Artificial Intelligence * MeSH
- Medical Informatics Applications MeSH
- Check Tag
- Humans MeSH
Nowadays, artificial intelligence (AI) affects our lives every single day and brings with it both benefits and risks for all spheres of human activities, including education. Out of these risks, the most striking seems to be ethical issues of the use of AI, such as misuse of private data or surveillance of people's lives. Therefore, the aim of this systematic review is to describe the key ethical issues related to the use of AI-driven mobile apps in education, as well as to list some of the implications based on the identified studies associated with this research topic. The methodology of this review study was based on the PRISMA guidelines for systematic reviews and meta-analyses. The results indicate four key ethical principles that should be followed, out of which the principle of algorithmovigilance should be considered in order to monitor, understand and prevent the adverse effects of algorithms in the use of AI in education. Furthermore, all stakeholders should be identified, as well as their joint engagement and collaboration to guarantee the ethical use of AI in education. Thus, the contribution of this study consists in emphasizing the need for joint cooperation and research of all stakeholders when using AI-driven mobile technologies in education with special attention to the ethical issues since the present research based on the review studies is scarce and neglected in this respect.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Mobile Applications * MeSH
- Educational Status MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- Systematic Review MeSH
IMPORTANCE: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. OBJECTIVES: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. EXPOSURE: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. MAIN OUTCOMES AND MEASURES: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. RESULTS: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account.
- MeSH
- Adult MeSH
- Trust MeSH
- Internationality MeSH
- Middle Aged MeSH
- Humans MeSH
- Hospitals MeSH
- Delivery of Health Care * MeSH
- Cross-Sectional Studies MeSH
- Surveys and Questionnaires MeSH
- Aged MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Tento článek zpracovává téma nových trendů a technologií v urologii, a to konkrétně v oblasti telemedicíny a umělé inteligence. Nejprve stručně pojednává o přínosech telemedicíny a jak mění pohled na vztah mezi lékařem a pacientem. Podrobněji se pak text věnuje především umělé inteligenci, jež se v současnosti dostává do popředí zájmu laické i odborné veřejnosti. Její potenciál v urologii je testován v mnoha studiích, především se zaměřením na uroonkologii, v menší míře pak také v oblasti benigních urologických onemocnění. Článek se snaží identifikovat nejvýznamnější pokroky v této rychle se rozvíjející oblasti, a zároveň předkládá současné limity jejího zapojení do klinické praxe.
This article explores the emerging trends and technologies in urology, focusing on telemedicine and artificial intelligence. It provides a brief overview of the benefits of telemedicine and its impact on the patient-physician interactions. The article subsequently explores in detail the use of artificial intelligence, which is currently gaining considerable interest from both general public and medical professionals. Its potential in urology has been tested in a number of clinical studies, particularly in the field of uro-oncology and, to a lesser extent, in benign urological diseases. The aim of this article is to identify the key advances in this rapidly evolving field, while also highlighting the current limitations of its implementation into clinical practice.
- MeSH
- Deep Learning MeSH
- Humans MeSH
- Robotic Surgical Procedures MeSH
- Machine Learning MeSH
- Telemedicine MeSH
- Artificial Intelligence MeSH
- Urologic Neoplasms diagnosis therapy MeSH
- Urology * trends MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
