This article aims to map the scientific production concerning the inclusion of people with disabilities in Science Education to promote a reflection on the production of this area. Bibliometric analysis is used to help understand what stage of research a particular subject is at. Publications on the topic indexed at the Web of Science Core Collection (WoS) were evaluated. A total of 119 articles published between 2009 and July 2019 were selected as dealing specifically with the subject. An increase in the number of articles associating Science teaching (ST) and Inclusive Education (IE) was noted. The journals that published the most, the most productive authors in the area and their collaboration networks were identified. A content analysis of the research was also carried out and the main investigated topics were pointed out. Educational levels, types of disabilities, central themes and specific science areas prevailing in the mapped research were also indicated. We conclude that, despite the growing number of articles, scientific production associating SE and IE is still small, concentrated, and not shared with the scientific community through scientific education journals, and that most research is focused on the use of methodologies and resources, and not on their development.

Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.

Background: The rapid evolution of artificial intelligence (AI) presents transformative potential for diagnostic medicine, offering opportunities to enhance diagnostic accuracy, reduce costs, and improve patient outcomes. Objective: This study aimed to assess the expected future impact of AI on diagnostic medicine by comparing global researchers’ expectations using 2 cross-sectional surveys. Methods: The surveys were conducted in September 2020 and February 2023. Each survey captured a 10-year projection horizon, gathering insights from >3700 researchers with expertise in AI and diagnostic medicine from all over the world. The survey sought to understand the perceived benefits, integration challenges, and evolving attitudes toward AI use in diagnostic settings. Results: Results indicated a strong expectation among researchers that AI will substantially influence diagnostic medicine within the next decade. Key anticipated benefits include enhanced diagnostic reliability, reduced screening costs, improved patient care, and decreased physician workload, addressing the growing demand for diagnostic services outpacing the supply of medical professionals. Specifically, x-ray diagnosis, heart rhythm interpretation, and skin malignancy detection were identified as the diagnostic tools most likely to be integrated with AI technologies due to their maturity and existing AI applications. The surveys highlighted the growing optimism regarding AI’s ability to transform traditional diagnostic pathways and enhance clinical decision-making processes. Furthermore, the study identified barriers to the integration of AI in diagnostic medicine. The primary challenges cited were the difficulties of embedding AI within existing clinical workflows, ethical and regulatory concerns, and data privacy issues. Respondents emphasized uncertainties around legal responsibility and accountability for AI-supported clinical decisions, data protection challenges, and the need for robust regulatory frameworks to ensure safe AI deployment. Ethical concerns, particularly those related to algorithmic transparency and bias, were noted as increasingly critical, reflecting a heightened awareness of the potential risks associated with AI adoption in clinical settings. Differences between the 2 survey waves indicated a growing focus on ethical and regulatory issues, suggesting an evolving recognition of these challenges over time. Conclusions: Despite these barriers, there was notable consistency in researchers’ expectations across the 2 survey periods, indicating a stable and sustained outlook on AI’s transformative potential in diagnostic medicine. The findings show the need for interdisciplinary collaboration among clinicians, AI developers, and regulators to address ethical and practical challenges while maximizing AI’s benefits. This study offers insights into the projected trajectory of AI in diagnostic medicine, guiding stakeholders, including health care providers, policy makers, and technology developers, on navigating the opportunities and challenges of AI integration.

Introduction: Rare genetic diseases affect millions of people worldwide. Most of them are caused by defective genes that impair quality of life and can lead to premature death. As genetic therapies aim to fix or replace defective genes, they are considered the most promising treatment for rare genetic diseases. Yet, as these therapies are still under development, it is still unclear whether they will be successful in treating these diseases. This study aims to address this gap by assessing researchers’ opinions on the future of genetic therapies for the treatment of rare genetic diseases. Methods: We conducted a global cross-sectional web-based survey of researchers who recently authored peer-reviewed articles related to rare genetic diseases. Results: We assessed the opinions of 1430 researchers with high and good knowledge about genetic therapies for the treatment of rare genetic diseases. Overall, the respondents believed that genetic therapies would be the standard of care for rare genetic diseases before 2036, leading to cures after this period. CRISPR-Cas9 was considered the most likely approach to fixing or replacing defective genes in the next 15 years. The respondents with good knowledge believed that genetic therapies would only have long-lasting effects after 2036, while those with high knowledge were divided on this issue. The respondents with good knowledge on the subject believed that non-viral vectors are more likely to be successful in fixing or replacing defective genes in the next 15 years, while most of the respondents with high knowledge believed viral vectors would be more successful. Conclusion: Overall, the researchers who participated in this study expect that in the future genetic therapies will greatly benefit the treatment of patients with rare genetic diseases. Plain Language Summary: A global survey of researchers on the future of genetic therapies for rare genetic diseases Rare genetic diseases are caused by defective genes that result from one or more mutations in the genome. Today, the therapeutic options for these diseases are limited, and there are approved treatments for about 5% of them. In the future, genetic therapies (a group of techniques developed to correct defective genes) are expected to revolutionize the treatment of rare genetic diseases. Although promising, most of these therapies are currently under development and have a long way to go before their efficacy and safety can be proved. The uncertainty surrounding this topic therefore means the success of genetic therapies in treating or curing rare genetic diseases is not yet assured. To address this knowledge gap, we surveyed 1430 researchers working in rare genetic diseases about the future of genetic therapies for the treatment of these diseases over the next 15 years. Most of them expected gene therapies to be the standard of care for rare genetic diseases before 2036 and to be able to cure them after this date. CRISPR-Cas9 was felt to be the gene editing approach that was most likely to succeed in fixing or replacing defective genes in the next 15 years. The respondents with high knowledge about gene therapies for the treatment of rare diseases believed gene therapies would have long-lasting effects before 2036, while those with good knowledge expected this to be the case only after 2036. The former believed in viral vectors and the latter in non-viral vectors to fix or replace defective genes in the next 15 years.