Развитие технологий искусственного интеллекта в онкологии и лучевой диагностике

Introduction. The widespread adoption of Artificial Intelligence (AI) technologies forms the core of the so-called Industrial Revolution 4.0.The aim of this study is to examine qualitative changes occurring over the last two years in the development of AI through an examination of trends in PubMed publications.Materials. All abstracts with keyword “artificial intelligence” were downloaded from PubMed database https://www.ncbi.nlm.nih.gov/pubmed/ in the form of .txt files. In order to produce a generalisation of topics, we classified present applications of AI in medicine. To this end, 78,420 abstracts, 5558 reviews, 304 randomised controlled trials, 247 multicentre studies and 4137 other publication types were extracted. (Figure 1). Next, the typical applications were classified.Results. Interest in the topic of AI in publications indexed in the PubMed library is increasing according to general innovation development principles. Along with English publications, the number of non-English publications continued to increase until 2018, represented especially by Chinese, German and French languages. By 2018, the number of non-English publications had started to decrease in favour of English publications. Implementations of AI are already being adopted in contemporary practice. Thus, AI tools have moved out of the theoretical realm to find mainstream application.Conclusions. Tools for machine learning have become widely available to working scientists over the last two years. Since this includes FDA-approved tools for general clinical practice, the change not only affects to researchers but also clinical practitioners. Medical imaging and analysis applications already approved for the most part demonstrate comparable accuracy with the human specialist. A classification of developed AI applications is presented in the article.Введение. Индустриальная революция 4.0 произошла во многом благодаря внедрению методов искусственного интеллекта.Цель исследования. Показать качественные перемены, которые произошли в последние 3 года в реализации методов искусственного интеллекта в здравоохранении путем исследования трендов по публикациям в базе данных PubMed.Материалы. Все резюме статей с ключевым словом “artificial intelligence” были загружены в текстовые файлы из базы данных https://www.ncbi.nlm.nih.gov/pubmed/. Путем обобщения ключевых слов мы классифицировали современные применения искусственного интеллекта в медицине. 78 420 резюме были извлечены из базы данных PubMed, в том числе 5558 обзоров, 304 рандомизированных исследования, 247 многоцентровых исследований. Затем были классифицированы типичные сферы применения.Результаты. Интерес к теме искусственного интеллекта в индексированных в PubMed публикациях растет согласно закону развития инноваций. Количество неанглоязычных публикаций увеличивалось до 2008 года и было представлено на китайском, немецком, французском и русском языках. После 2008 года количество неанглоязычных публикаций снизилось в пользу англоязычных.Выводы. В последние два-три года искусственный интеллект в практике принятия решений в медицине стал иметь реальное практическое применение. Кроме того, инструменты для создания систем принятия решений на базе методик искусственного интеллекта стали уже не диковинными и доступны не только математикам. Американское управление пищевыми продуктами и лекарственными препаратами (FDA) одобрило ряд приложений к клинической практике. Это еще одна перемена, которая затронула не только ученых, но и практиков. Большинство таких приложений используется для анализа медицинских изображений, в том числе и в онкологии, и демонстрирует сравнимую точность с человеком специалистом. В статье представлена разработанная классификация применения технологий искусственного интеллекта

Evaluating the Information Usefulness of Online Health Information for Third-party Patients

Online health interactions (OHIs) can benefit patients, physicians, and society. However, little research has been conducted that studies the social value of OHIs for third-party patients who view previous OHIs concerning similar health issues to theirs. Drawing on the literature on social support and information uncertainty, this study established a theoretical model to explore the roles of treatment information, prevention information, and emotional support in determining information usefulness perceived by third-party patients, and whether such relationships are contingent on information uncertainty. The model was tested using “health questions and answers” textual data from 1,848 OHIs. The results indicate that prevention information and emotional support significantly improve information usefulness perceived by third-party patients. When the level of information uncertainty regarding physicians’ replies is high, the effect of treatment information is strengthened and the effect of emotional support is weakened, indicating both positive and negative contingent roles of information uncertainty. This study has implications for practitioners and also contributes to the literature on online health information, social support, information science, and information uncertainty

Process Models of Interrelated Speech Intentions from Online Health-related Conversations

International audienceBeing related to the adoption of new beliefs, attitudes and, ultimately, behaviors, analyzing online communication is of utmost importance for medicine. Multiple health care, academic communities, such as information seeking and dissemination and persuasive technologies, acknowledge this need. However, in order to obtain understanding, a relevant way to model online communication for the study of behavior is required. In this paper, we propose an automatic method to reveal process models of interrelated speech intentions from conversations. Specifically, a domain-independent taxonomy of speech intentions is adopted, an annotated corpus of Reddit conversations is released, supervised classifiers for speech intention prediction from utterances are trained and assessed using 10-fold cross validation (multi-class, one-versus-all and multi-label setups) and an approach to transform conversations into well-defined, representative logs of verbal behavior, needed by process mining techniques, is designed. The experimental results show that: 1) the automatic classification of intentions is feasible (with Kappa scores varying between 0.52 and 1); 2) predicting pairs of intentions, also known as adjacency pairs, or including more utterances from even other heterogeneous corpora can improve the predictions of some classes; and 3) the classifiers in the current state are robust to be used on other corpora, although the results are poorer and suggest that the input corpus may not suciently capture varied ways of expressing certain intentions. The extracted process models of interrelated speech intentions open new views on grasping the formation of beliefs and behavioral intentions in and from speech, but in-depth evaluation of these models is further required