Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language can often provide a much richer learning medium for LLMs, compared with policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates natural language reflection to learn high-level rules from trial and error. Given any AI system containing one or more LLM prompts, GEPA samples system-level trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As a result of GEPA's design, it can often turn even just a few rollouts into a large quality gain. Across four tasks, GEPA outperforms GRPO by 10% on average and by up to 20%, while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10% across two LLMs, and demonstrates promising results as an inference-time search strategy for code optimization.

Described simply, artificial intelligence (AI) is a field that combines computer science and robust data sets, to enable problem-solving. The umbrella term encompasses the subfields of machine learning and the more recently developed deep learning, which itself is a subfield of machine learning. Both use AI algorithms to create expert systems that make predictions or classifications based on input data. The first reports of AI use in radiology date back to 1992 when it was used to detect microcalcifications in mammography1 and was more commonly known as computer-aided detection. It wasn't until around the mid-2010s that it really started to be seen as a potential solution to the daily challenges, such as volume burden, faced by radiologists.

They already are, but may not realize it since many tools are used by clinicians behind the scenes in radiology and imaging, explained Peter Shen, head of digital health at Siemens Healthineers North America. But increasing personalized medical care by using AI tools is something Siemens is continuing to refine and prioritize. "Our strategy for AI goes beyond imaging and pattern recognition," Shen said. "The informed diagnostics we derive from AI allow us to design better ways to take care of patients. For us, it is about more than efficiency and more than just decision-making. We want to start to drive personalized medicine toward the patients themselves and create accessibility in medical care."

Air Recon DL's benefits extend to nearly all magnetic resonance imaging (MRI) clinical procedures. The platform covers all anatomies, enabling better image quality, shorter scan times and improved patient experience. GE Healthcare said in a news release that the solution's compatibility expands from 2D to 3D imaging sequences. This allows physicians to diagnose patients with an improved signal-to-noise ratio (SNR) and sharpness. Meanwhile, it said the 3D imaging provides for more clinical efficiency by eliminating the need for multiple 2D acquisitions.

Offering the promise of improved efficiency with in-room workflows, reduced strain with patient positioning and robust image quality, GE Healthcare has introduced a new fixed X-ray system with Definium 656 HD. In order to alleviate some of the physical demands with patient positioning and ensure optimal image capture, the Definium 656 HD system offers a variety of features. GE Healthcare said these features include: 5-axis motorization and automatic positioning; a 12" touchscreen on the tube head console that facilitates automated adjustments to in-room workflows; and 3D camera technology through the system's Intelligent Workflow Suite that enables more consistent image quality and prevents unnecessary repeat X-rays. The Definium 656 HD platform incorporates artificial intelligence (AI) to provide enhanced anatomic detail and clarity through the use of Helix 2.2 advanced image processing and 100 um FlashPad HD detectors, according to the company. GE Healthcare said other benefits of the Definium 656 HD system include multi-level image slice capabilities with the VolumeRAD Digital Tomosynthesis feature and the enhancement of the Auto Image Paste feature with AutoSpine, which facilitates precise and efficient stitching of long images.

Background \& purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spectral Mammography (CESM) where access to large databases is complex. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases. Material \& methods: A large CESM diagnostic dataset with biopsy-proven lesions was collected from various hospitals and different acquisition systems. The annotated data were split on a patient level for the training (55%), validation (15%) and test (30%) of a deep neural network with a state-of-the-art detection architecture. Free Receiver Operating Characteristic (FROC) was used to evaluate the model for the detection of 1) all lesions, 2) biopsied lesions and 3) malignant lesions. ROC curve was used to evaluate breast cancer classification. The metrics were finally compared to clinical results. Results: For the evaluation of the malignant lesion detection, at high sensitivity (Se>0.95), the false positive rate was at 0.61 per image. For the classification of malignant cases, the model reached an Area Under the Curve (AUC) in the range of clinical CESM diagnostic results. Conclusion: This CAD is the first development of a lesion detection and classification model for CESM images. Trained on a large dataset, it has the potential to be used for helping the management of biopsy decision and for helping the radiologist detecting complex lesions that could modify the clinical treatment.

In heavily regulated industries such as healthcare, digital innovation can be slow to progress. However, once organizations push towards digital transformation and innovation, the benefits that can be achieved such as revenue growth, patient volume, and cost of care can provide tremendous value. Healthcare organizations are looking for an approach to cost-effective and technically efficient build-out to help on their digital transformation journeys. With investments shifting from core EMRs to infrastructure solutions that enable flexibility and adaptability, healthcare organizations are looking to digital innovation to solve these key issues. In an upcoming Enterprise Data & AI presentation on May 5, 2022, Vignesh Shetty, SVP & GM Edison AI And Platform, GE Healthcare Digital will discuss GE Healthcare's digital health platform and how it's helping companies in the healthcare sector on their AI and data journey.

GE Healthcare and Optellum today announced that they have signed a letter of intent to collaborate to advance precision diagnosis and treatment of lung cancer. GE Healthcare is a global leader in medical imaging solutions. Optellum is the leader in AI decision support for the early diagnosis and optimal treatment of lung cancer. This press release features multimedia. Together, the companies are seeking to address one of the largest challenges in the diagnosis of lung cancer, helping providers to determine the malignancy of a lung nodule: a suspicious lesion that may be benign or cancerous.

The end of June saw the announcement that Peter Thomas would be stepping into the chief clinical information officer (CCIO) role at Moorfields Eye Hospital NHS Foundation Trust in August. The consultant joined the trust in 2017 and took a special interest in machine learning and artificial intelligence, pioneering the hospital's use of digital medicine and telemedicine. In his capacity as CCIO, Thomas will be in charge of raising awareness of clinical informatics as an important element in safe, high-quality patient care. He said: "I am delighted to be offered the role of chief clinical information officer at Moorfields and I hope to use this opportunity to use digital medicine in innovative ways to help our patients receive the best care possible." University Hospitals of Leicester NHS Trust has announced Richard Mitchell will take up the position as the trust's chief executive from Autumn 2021.

Learn how to simplify complex steps with automated and advanced clinical tools to enable fast assessments, support life-saving decisions, and help monitor patient progress even in unpredictable, chaotic environments. AI tools help drive consistency from user to user, whether you are an ultrasound novice or an expert, there's a Venue Family system to fit your needs. Learn how to simplify complex steps with automated and advanced clinical tools to enable fast assessments, support life-saving decisions, and help monitor patient progress even in unpredictable, chaotic environments. AI tools help drive consistency from user to user, whether you are an ultrasound novice or an expert, there's a Venue Family system to fit your needs.