The aim of this paper is to map the scientific landscape related to cancer research worldwide between 2012 and 2017. We use scientific publication data from Web of Science Core Collection and combine bibliometrics and social network analysis techniques to identify the most relevant journals, research areas, countries and research organizations in cancer scientific landscape. The results show: Oncotarget as the journal with most publications; a significant increase in China’s publications, reaching United States’ publications in 2017; MD Cancer Center, University of California and Harvard University as organizations with most publications; cell biology as the most frequent research area; breast, lung and colorectal cancer as the most frequent keywords; high density of co-authorship between organizations in the West, especially in the US, and low density between organizations in Asian and lower and medium income countries. Our findings can be used to guide a global knowledge platform guiding policy, planning and funding decisions as well as to establish new institutional collaborations.

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.