Segmed Blogs

Dr. Aline Lutz makes her mark as one of Brazil's top contributors to digital health research The conversation around digital health has been rapidly evolving, driven by the integration of cutting-edge technology into healthcare practices. Among the brightest figures in this transformation is Aline Lutz, Vice President of Medical Affairs at Segmed . Recently, she achieved a remarkable distinction, being named one of Brazil’s top 20 scientists in the field of digital health by a peer-reviewed article published by researchers from the Moinhos de Vento Hospital in Porto Alegre (Brazil) in Frontiers in Digital Health [1].  Chagas and colleagues conducted a bibliometric analysis aiming to evaluate the global scientific production in digital health with special attention to Latin America and Brazil. They included a total of 51,723 publications (global), covering 2,410 from Latin America, and 1,317 from Brazil. Based on these data the authors concluded that the scientific publications in ...

Introduction  Development of foundation models in healthcare has increased considerably over the recent past, especially as a way of enhancing critical clinical workflow optimization. Foundation models are a form of generative artificial intelligence (AI). Applications like large language models (LLMs) are common examples of foundation models. It is becoming apparent that the application of foundation models to decision support systems enhances clinical workflows, supports diagnosis, and enables personalized care. Foundation models and other recent developments related to artificial intelligence (AI) have a substantial impact in healthcare, helping with more personalized and efficient patient management. Since these innovative technologies require large amounts of data, adoption of real-world data has become essential. And with newer sources of real-world data such as real-world imaging data (RWiD) coming available at scale, innovation in healthcare is ramping up tremendously. RWi...

Discover how Segmed’s tokenized Real-World Imaging Data (RWiD) enables the tuning of the healthcare foundation model for maximizing healthcare outcomes. Introduction to healthcare foundation models The rapid advancement in highly adaptable and reusable foundation models promises to bring new capabilities in the field of medicine.  Healthcare foundation models (HFMs)  are large, pre-trained AI models used for multiple applications in healthcare. The models are first trained on massive, generalized datasets of data, allowing them to learn and capture patterns across a wide range of  therapeutic areas . Healthcare foundation models offer solutions to complex tasks, such as streamlining clinical workflows by extracting information from unstructured data, identifying pathologies, and generating clinical reports. Potential applications of HFMs include real-time patient interaction through virtual medical chatbots that help in support, triage, and education. One of the major app...

Africa has more than 24% of the global burden of disease, but has access to only 3% of the world’s healthcare workers . The Sub-Saharan Africa region has about 0.19 doctors per 1,000 individuals as compared to the United States, which has 2.6 per 1,000 . That means there is 1 doctor per 5,263 individuals in Sub-Saharan Africa as compared to the United States, which has 1 doctor for every 385 individuals. Sub-Saharan Africa is not an anomaly when it comes to these ratios of doctors per population in developing parts of the world. According to the WHO, Papua New Guinea has 1 doctor for every 18,903 individuals, Saint Lucia has 1 doctor for every 9,497 individuals, and Cambodia has 1 doctor for every 5,945 individuals. Other countries—aside from the Sub-Saharan Africa region and the other three aforementioned countries—that face these extreme ratios include Vanuatu, Madagascar, Federated States of Micronesia, Solomon Islands, Haiti, Afghanistan, Yemen, Honduras, Samoa, Guatemala, Bhutan,...

Introduction This article explores the Vision Language Foundation model, a model that is transforming the way chest X-rays are generated in medical research. Learn more about how it works as well as its benefits and drawbacks. Background With the evolution of artificial intelligence and image generation, the applications of these models have been a point of contention. AI and Stable Diffusion can produce realistic, variable-controlled images, and as such, their potential applications have drawn the attention of healthcare professionals. Currently, it is unclear how many medical concepts and image features found in healthcare applications are incorporated in these models. For instance, if you only use Stable Diffusion and no other modifications are made, you get very artificial-looking images, more akin to stock photos or fabricated visualizations. Before anyone can think about how to adapt these models to produce outputs that are more useful, we should also consider why someone would w...

The History of Artificial Intelligence: Artificial intelligence, commonly known as AI, has become one of the most common “buzzwords” of the new millennium. Although it is so frequently used in conversation, news, and technology, many still don’t know what it is exactly. The goal for this blog post is to inform the audience about how AI works, the different types of AI, as well as the major subgroups that exist today, and why AI technology matters to Segmed. Artificial intelligence was first coined in 1956 by John McCarthy when he held the Dartmouth Summer Research Project on Artificial Intelligence, a conference at Dartmouth College spanning the entire summer. This colloquium was a workshop filled with scientists who came to the conclusion that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,” thus prompting the emergence of the field of AI. They also decided that the key factors for a...