Desafios e avanços na personalização diagnóstica e terapêutica na era da inteligência artificial na saúde

The integration of artificial intelligence (AI) and machine learning (ML) in medicine represents a rapidly growing field, promising significant advances in diagnostic and treatment processes. Given this scenario, this integrative review seeks to consolidate and critically analyze the available scientific evidence on the application of these innovative technologies in medical practice. The methodology adopted for this integrative review involved a comprehensive search of the main databases, such as PubMed, Scielo and ScienceDirect, using the relevant descriptors, such as "Artificial Intelligence", "Machine Learning", "Clinical Diagnosis", "Machine Learning" and "Deep Learning". The careful selection of references included relevant studies that address the application of AI and ML in various domains of medicine, with a special focus on the references indicated in Vancouver in this abstract. The results of this review reveal a wide range of successful applications of AI and AM in medical diagnosis and treatment. Studies such as Wang et al. (2019) highlight the progress and challenges of using deep learning in medicine, while work by Erickson et al. (2017) highlights the effectiveness of ML in medical imaging, contributing to advances in clinical practice. Ethical approaches and future impacts on the actions of healthcare professionals, as discussed by Ahuja (2019) and Farhud and Zokaei (2021), emerge as crucial points in the integration of these technologies. The conclusion of this integrative review reinforces the significant transformation provided by the integration of AI and AM in medicine, offering faster and more accurate diagnoses, as well as outlining intrinsic ethical challenges. Patient privacy and ethical considerations become critical factors in this scenario. This comprehensive analysis highlights the continued need for responsible research and development, promoting advances that optimize clinical efficacy and ensure the trust of healthcare professionals and patients in the face of these transformative innovations.A integração de inteligência artificial (IA) e aprendizado de máquina (AM) na medicina representa um campo em rápido crescimento, prometendo avanços significativos nos processos de diagnóstico e tratamento. Diante desse cenário, a presente revisão integrativa busca consolidar e analisar criticamente as evidências científicas disponíveis sobre a aplicação dessas tecnologias inovadoras na prática médica. A metodologia adotada para esta revisão integrativa envolveu uma busca abrangente nas principais bases de dados, como PubMed, Scielo e Scopus, utilizando os descritores pertinentes, tais como "Inteligência Artificial", "Aprendizado de Máquina", "Diagnóstico Clínico", "Machine Learning" e "Deep Learning". A seleção criteriosa das referências incluiu estudos relevantes que abordam a aplicação de IA e AM em diversos domínios da medicina, com foco especial nas referências indicadas em Vancouver neste resumo. Os resultados desta revisão revelam uma ampla gama de aplicações bem-sucedidas de IA e AM em diagnósticos e tratamentos médicos. Estudos como o de Wang et al. (2019) destacam os progressos e desafios do uso de deep learning na medicina, enquanto trabalhos de Erickson et al. (2017) evidenciam a eficácia do AM em imagens médicas, contribuindo para avanços na prática clínica. Abordagens éticas e impactos futuros na atuação dos profissionais de saúde, conforme discutido por Ahuja (2019) e Farhud e Zokaei (2021), emergem como pontos cruciais na integração dessas tecnologias. A conclusão desta revisão integrativa reforça a transformação significativa proporcionada pela integração de IA e AM na medicina, oferecendo diagnósticos mais rápidos e precisos, bem como delineando desafios éticos intrínsecos. A privacidade do paciente e as considerações éticas tornam-se fatores críticos nesse cenário. Esta análise abrangente destaca a necessidade contínua de pesquisa e desenvolvimento responsável, promovendo avanços que otimizem a eficácia clínica e garantam a confiança dos profissionais de saúde e dos pacientes diante dessas inovações transformadoras

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora

Diminishing benefits of urban living for children and adolescents’ growth and development

Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified