Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

Radiologists produce unstructured data that could be valuable for clinical care when consumed by information systems. However, variability in style limits usage. Study compares performance of system using domain-adapted language model (RadLing) and general-purpose large language model (GPT-4) in extracting common data elements (CDE) from thoracic radiology reports. Three radiologists annotated a retrospective dataset of 1300 thoracic reports (900 training, 400 test) and mapped to 21 pre-selected relevant CDEs. RadLing was used to generate embeddings for sentences and identify CDEs using cosine-similarity, which were mapped to values using light-weight mapper. GPT-4 system used OpenAI's general-purpose embeddings to identify relevant CDEs and used GPT-4 to map to values. The output CDE:value pairs were compared to the reference standard; an identical match was considered true positive. Precision (positive predictive value) was 96% (2700/2824) for RadLing and 99% (2034/2047) for GPT-4. Recall (sensitivity) was 94% (2700/2876) for RadLing and 70% (2034/2887) for GPT-4; the difference was statistically significant (P<.001). RadLing's domain-adapted embeddings were more sensitive in CDE identification (95% vs 71%) and its light-weight mapper had comparable precision in value assignment (95.4% vs 95.0%). RadLing system exhibited higher performance than GPT-4 system in extracting CDEs from radiology reports. RadLing system's domain-adapted embeddings outperform general-purpose embeddings from OpenAI in CDE identification and its light-weight value mapper achieves comparable precision to large GPT-4. RadLing system offers operational advantages including local deployment and reduced runtime costs. Domain-adapted RadLing system surpasses GPT-4 system in extracting common data elements from radiology reports, while providing benefits of local deployment and lower costs.

Can it perform 24/7 support for practicing clinicians? 2. Findings: We installed an AI solution for Chest X-ray in a given structure (MVZ Uhlenbrock & Partner, Germany). We could demonstrate the practicability, performance, and benefits in 10 connected clinical sites. The system performs in a robust manner - supporting radiologists and clinical colleagues in their important decisions-in practises and hospitals regardless of the user and X-ray system type producing the image-data. Introduction As one of the worldwide most taken X-ray procedures, chest X-ray (CXR) is one of the most important and also most demanding modalities in daily medical imaging. Due to its accessibility and fast acquisition, chest radiographs are frequently used as a first diagnostic step, deciding on further patient treatment [1,2].

The pace of digitalization in healthcare is accelerating. AI-based reading solutions that support radiologists in managing their growing workload and delivering confident diagnoses will become particularly more important over the next years. In healthcare, the demand for diagnostic services is steadily growing – while the number of available experts is decreasing. Additionally, diagnostics and treatment are becoming increasingly complex. As a result, software solutions using artificial intelligence (AI) are on the rise.

Cerebriu, a provider of artificial intelligence (AI) solutions for automating radiology workflows, and Siemens Healthineers, the world's leading manufacturer of magnetic resonance imaging (MRI) scanners, announced their partnership to integrate Cerebriu's patented Smart Protocol for Brain MRI-workflow automation software with the MRI machines of Siemens Healthineers. The application is planned to be incorporated into the Open Workflow interface, which will allow solutions to be seamlessly integrated directly into the MRI examination workflow. Smart Protocol for Brain is designed to perform in-process detection and protocol automation during image acquisition, providing personalized diagnostic imaging ensuring the right data for differential diagnosis. ''We are excited and proud to become the first AI technology company for MR Brain workflow automation Siemens Healthineers has entrusted to integrate into the Open Workflow interface," said Robert Lauritzen, Chief Executive Officer at Cerebriu. "We automate radiology workflows to increase accessibility and improve quality of care, carefully balancing increased throughput with sustainable workloads to help tackle the increasing demand for neuroimaging," said Akshay Pai, Chief Technology Officer.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. The health care industry is starting to adopt digital twins to improve personalized medicine, health care organization performance, and new medicines and devices. Although simulations have been around for some time, today's medical digital twins represent an important new take. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and health care organizations, as well as drug and device manufacturers.

Prisma Health on Tuesday announced a 10-year partnership with Siemens Healthineers to use artificial intelligence to help physicians better diagnose their patients and devise treatment plans. The partnership means employing the latest technology across all Prisma sites, said CEO Mark O'Halla. "The whole goal of this relationship is leveraging technology and our relationships with each other to significantly improve access … by improving productivity and throughput," he said. "We are leveraging all the artificial intelligence expertise that Siemens is bringing to the table and teaming up with clinicians." The idea is that clinicians will make more informed decisions, ultimately allowing for faster and more precise diagnoses and treatment plans, he said. The arrangement, whose financial details were not disclosed, will focus on next generation medical technology, said Dave Pacitti, president and head of the Americas for Siemens Healthineers, the parent company for several medical technology companies.

Looked at on a world scale, the Covid-19 pandemic will continue to deliver shocks to global supply chains for some time to come. Even if the public health crisis abates in the UK, our economy is part of a global economy, and UK corporate IT will have its work cut out in supporting companies as they are forced to re-forge supply chains, perhaps over and over again, and at short notice. The crisis has provoked some rethinking of how the world economy ought to work, with an emphasis on the desirability of a shift from efficiency – doing things "just in time" – to resilience – building in more slack. The FT's Rana Faroohar provides an account of such rethinking in an article entitled From'just in time' to'just in case' published earlier this year. In the discussions which lie behind this article there are different emphases on a spectrum of opinion: some say we can have both efficiency and resilience equally, others that there is a choice to be made for one or the other, and yet others say it's a matter of balance, of trading off. Tony Harris, global vice-president of business network solutions at SAP, says it has to be a combination. "You wouldn't want to move to a resilient network or supply chain that wasn't also efficient," he says.

Among health care systems and payers--both government and private--this term implies unnecessary risk to patients, diminished quality of care, and increased health care costs. From the numerous studies and papers published on the topic, a common description emerges: unwarranted variations are not directly due to the inherent variations of a given patient; rather to the variations in the processes and conditions of how care is delivered, especially when it results in inconsistencies in the adherence to and administering of evidence-based guidelines of care. Consider the variations that exist among complex disease pathways such as oncology. In lung cancer, over 75% of patients are at an advanced state of disease when diagnosed, making it the leading cause of cancer deaths among both men and women. Thus, early detection, either as a focused diagnosis or an incidental finding, is key for an optimal five-year survival rate, as is the rapid commencement of a treatment protocol. At the other end of the spectrum is prostate cancer.

Diagnosing emphysema and classifying its severity have long been more art than science. "Everybody has a different trigger threshold for what they would call normal and what they would call disease," said U. Joseph Schoepf, M.D., director of cardiovascular imaging for MUSC Health and assistant dean for clinical research in the Medical University of South Carolina College of Medicine. And until recently, scans of damaged lungs have been a moot point, he said. "In the past, if you lost lung tissue, that was it. The lung tissue was gone, and there was very little you could do in terms of therapy to help patients," he said.