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.

Cystic fibrosis is a life-threatening disease that affects at least 100,000 people worldwide. It is caused by a defect in the cystic fibrosis transmembrane regulator (CFTR) gene and presently, 360 CFTR-causing mutations have been identified. Since the discovery of the CFTR gene, the expectation of developing treatments that can substantially increase the quality of life or even cure cystic fibrosis patients is growing. Yet, it is still uncertain today which developing treatments will be successful against cystic fibrosis. This study addresses this gap by assessing the opinions of over 524 cystic fibrosis researchers who participated in a global web-based survey. For most respondents, CFTR modulator therapies are the most likely to succeed in treating cystic fibrosis in the next 15 years, especially through the use of CFTR modulator combinations. Most respondents also believe that fixing or replacing the CFTR gene will lead to a cure for cystic fibrosis within 15 years, with CRISPR-Cas9 being the most likely genetic tool for this purpose.

Technologies of 3D and 4D bioprinting make it possible to restore or replace tissues and organs, solving the problem of the lack of donor resources and reducing the risks of implant rejection. This article presents the results of a two-stage global survey of specialists in tissue engineering on the prospects of bioprinting in preclinical studies and clinical practice. A picture of possible tracks and horizons upon which the implementation of the considered solutions is possible is presented. According to the results of the survey, in the next two decades it will be possible to recreate tissues and organs suitable for implantation and drug testing. There will be a market for bioprinted products, the problem of organ shortages and adverse reactions to drugs will be solved. These changes may significantly affect not only the practice of biomedical research, drug testing, and medicine, but also the healthcare sector in general, which implies the need for a preventive review of current policies. A practical and accessible tool for identifying and interviewing a large number of experts around the world is proposed, which may be useful for new Foresight studies.

This study aims to analyze the determinants of private investment in Brazil from 1971 to 2019 based on the peculiarities of emerging economies. These economies have characteristics that are different from those observed in developed countries and constitute the axioms that support the empirical studies usually carried out on the subject. The uncertainty of the political-economic environment, the low availability of credit, the scarcity of foreign exchange, exchange rate policies, and the precariousness of infrastructure are factors that influence investment decisions in emerging economies. Therefore, they should be part of empirical studies. The results of the econometric analysis—based on the Autoregressive Vectors (VAR) methodology and the Error Correction Model (VECM)—indicate that, both in the short and long term, public investment complemented private sector investment (crowding-in effect). This result indicates that public sector investments were channeled into infrastructure or into areas in which the private sector had no interest or capacity to act. This effect is confirmed by the positive result that investments in infrastructure have on the private sector in Brazil.

Resumo Com base na teoria de Thirlwall e considerando o aprofundamento das relações comerciais internacionais nas últimas décadas, este estudo buscou identificar quais países integrantes do bloco econômico dos BRICS têm seu crescimento econômico limitado pelo Balanço de Pagamentos. Para tanto, foi conduzida uma análise empírica utilizando a metodologia ARDL (Autoregressive Distributed Lag), abrangendo o período de 1961 a 2023. Os resultados apontam que, entre os países analisados, Rússia e Índia apresentam crescimento econômico restringido pelo comércio internacional.

Este estudo apresenta uma análise acerca dos determinantes do investimento privado no Brasil entre 1971 e 2016. São apresentadas algumas teorias de investimento para embasar a análise empírica, dentre essas a teoria neoclássica, a teoria keynesiana e a ambiguidade que o gasto público exerce em economias em desenvolvimento. Através da metodologia dos vetores autorregressivos, observa-se que o gasto público e a ampliação da infraestrutura impactam positivamente sobre as decisões de investimento do setor privado. Por outro lado, o aumento do custo do capital assim como dos impostos influenciam negativamente o a formação bruta de capital fixo do setor privado.