Strategies and bottlenecks to tackle infodemic in public health: a scoping review.

Background: The World Health Organization defines "infodemic" as the phenomenon of an uncontrolled spread of information in digital and physical environments during a disease outbreak, causing confusion and risk-taking behaviors that can harm health. The aim of this scoping review is to examine international evidence and identify strategies and bottlenecks to tackle health-related fake news. Methods: We performed a scoping review of the literature from 1 January 2018 to 26 January 2023 on PubMed, Web of Science, and Scopus electronic databases. We also performed a search of grey literature on institutional websites. The research question has been defined according to the PCC (population, concept, and context) mnemonic for constructing research questions in scoping reviews. Results: The overall research in the scientific databases yielded a total of 5,516 records. After removing duplicates, and screening the titles, abstracts, and full texts, we included 21 articles from scientific literature. Moreover, 5 documents were retrieved from institutional websites. Based on their content, we decided to group recommendations and bottlenecks into five different and well-defined areas of intervention, which we called strategies: "foster proper communication through the collaboration between science and social media companies and users," "institutional and regulatory interventions," "check and debunking," "increase health literacy," and "surveillance and monitoring through new digital tools." Conclusion: The multidisciplinary creation of standardized toolkits that collect recommendations from the literature and institutions can provide a valid solution to limit the infodemic, increasing the health education of both citizens and health professionals, providing the knowledge to recognize fake news, as well as supporting the creation and validation of AI tools aimed at prebunking and debunking.

Scoping review of the current landscape of AI-based applications in clinical trials.

Background: Clinical trials are essential for bringing new drugs, technologies and procedures to the market and clinical practice. Considering the design and the four-phase development, only 10% of them complete the entire process, partly due to the increasing costs and complexity of clinical trials. This low completion rate has a huge negative impact in terms of population health, quality of care and health economics and sustainability. Automating some of the process' tasks with artificial intelligence (AI) tools could optimize some of the most burdensome ones, like patient selection, matching and enrollment; better patient selection could also reduce harmful treatment side effects. Although the pharmaceutical industry is embracing artificial AI tools, there is little evidence in the literature of their application in clinical trials. Methods: To address this issue, we performed a scoping review. Following the PRISMA-ScR guidelines, we performed a search on PubMed for articles on the implementation of AI in the development of clinical trials. Results: The search yielded 772 articles, of which 15 were included. The articles were published between 2019 and 2022 and the results were presented descriptively. About half of the studies addressed the topic of patient recruitment; 12 articles reported specific examples of AI applications; five studies presented a quantitative estimate of the effectiveness of these tools. Conclusion: All studies present encouraging results on the implementation of AI-based applications to the development of clinical trials. AI-based applications have a lot of potential, but more studies are needed to validate these tools and facilitate their adoption.