Large language models (LLMs) show promise for supporting clinical decision-making in complex fields such as rheumatology. Our evaluation shows that smaller language models (SLMs), combined with retrieval-augmented generation (RAG), achieve higher diagnostic and therapeutic performance than larger models, while requiring substantially less energy and enabling cost-efficient, local deployment. These features are attractive for resource-limited healthcare. However, expert oversight remains essential, as no model consistently reached specialist-level accuracy in rheumatology.

The introduction of machine learning marks a pivotal shift, presenting Nailfold capillaroscopy is a well-established method for automated medical image analysis as a promising alternative assessing health conditions, but the untapped potential of automated due to its higher accuracy compared to traditional image medical image analysis using machine learning remains processing algorithms[5]. Recent studies have attempted to despite recent advancements. In this groundbreaking use single deep-learning models for tasks such as nailfold study, we present a pioneering effort in constructing a comprehensive capillary segmentation[4, 8], measurement of capillary size dataset--321 images, 219 videos, 68 clinic reports, and density[5], and white cell counting[9]. Despite notable with expert annotations--that serves as a crucial resource achievements, the untapped potential of automated medical for training deep-learning models. Leveraging this image analysis persists due to the urgent need for annotated dataset, we propose an end-to-end nailfold capillary analysis and extensive datasets essential for effective training and pipeline capable of automatically detecting and measuring diverse fine-tuning deep neural networks.

Artificial intelligence is exactly that — it would recognize patterns that you might not otherwise recognize if you relied only on your small …

Artificial intelligence (AI) detected axial spondyloarthritis (axSpA) in radiographic scans of sacroiliac joints as accurately as human experts, shows a new study. The findings have the potential to greatly expand access to accurate axSpA diagnoses for patients in areas that lack trained experts on the condition, said Denis Poddubnyy, M.D., head of rheumatology at Charité University in Germany. The findings will be presented on Monday at the annual meeting of the American College of Rheumatology. AxSpA is typically diagnosed by radiograph, but successfully identifying signs of axSpA in a scan can be difficult. Readings by specialists with little experience in rheumatology can be inaccurate, but then, not all healthcare sites have easy access to physicians with rheumatology expertise.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.

Most high-performance expert systems rely primarily on an ability to represent surface knowledge about associations between observable evidence or data, on the one hand, and hypotheses or classifications of interest, on the other. Although the present generation of practical systems shows that this architectural style can be pushed quite far, the limitations of current systems motivate a search for representations that would allow expert systems to move beyond the prevalent "symptom-disease" style. One approach that appears promising is to couple a rule-based or associational system module with some other computational model of the phenomenon or domain of interest. According to this approach, the domain knowledge captured in the second model would be selected to complement the associational knowledge represented in the first module. Simulation models have been especially attractive choices for the complementary representation because of the causal relations embedded in them (Brown & Burton, 1975; Cuena, 1983).

Results of preclinical studies by investigators at the Medical University of South Carolina (MUSC) reported in the August 2016 issue of Arthritis & Rheumatology demonstrate for the first time that including novel biomarkers in lupus nephritis (LN) prognostic models significantly increases their power to predict therapeutic efficacy. Identifying biomarker models with sufficient predictive power is a critical step toward developing clinical decision-making tools that can rapidly identify patients who require a change in therapy and potentially reduce onset of renal fibrosis during induction therapy. Approximately half of all patients with systemic lupus erythematosus (SLE) develop LN, an immune complex-mediated glomerulonephritis. Lupus nephritis, in turn, leads to renal failure in up to 50% of patients within five years. American College of Rheumatology guidelines recommend changing LN treatment after six months of induction therapy if response to therapy is not achieved.

Specialization and generalization are accomplished by adding or deleting elements in these lists. The use of symbolic categories of belief (definite, probable, and possible) provides a specifiable means for manipulating the rules. While based on a simple idea, the SEEK program convincingly demonstrates the value of a rich('v structured representation and of reasoning from cases as a way of constructing a model. That is, exjJert knowledge is inseparable from case experience (Schank, 1983), in so far as knov.Jledge explains the cases. The use of a knowledge base to provide an explanatm), model has characterized other recent AIM work as well (cf.

Every year, as computers become more powerful and less expensive, increasing amounts of health care data are recorded on them. Motivation for collecting data routinely into ambulatory and hospital medical record systems comes from all quarters. Health practitioners require sets of data for clinical management of individual patients. Hospital administrators require them for billing and resource allocation.