PubMed چکیده/رکورد

Essential Informatics Tools and Computing Infrastructure for Big Data to Advance Artificial Intelligence in Rheumatology.

Jeffrey R Curtis, Emily Holladay, Tapan Mehta, John Osborne• 2026• Rheumatic diseases clinics of North America

استودیوی صوتی مقاله

پخش حرفه‌ای فارسی و انگلیسی

در حال بررسی نسخه‌های صوتی ذخیره‌شده…

صوت تولیدشده با هوش مصنوعی است. برای کاربرد علمی یا درمانی، متن و منبع اصلی را بررسی کنید.

خواندن هوشمند فارسی و انگلیسی در حال آماده‌سازی صداهای مرورگر…

تنظیم صدای طبیعی و سرعت

صدای فارسی صدای انگلیسی سرعت خواندن

صداهایی که در نامشان «Natural»، «Neural» یا «Online» دیده می‌شود معمولاً طبیعی‌ترند. انتخاب صدا به صداهای نصب‌شده در ویندوز و مرورگر شما بستگی دارد.

چکیده اصلی

Rheumatic diseases are chronic, heterogeneous, and longitudinal, and assembling real-world evidence for effectiveness and safety for their study is best served by integrating diverse data types. This article describes the infrastructure required to support scalable, trustworthy artificial intelligence (AI) in rheumatology, emphasizing data acquisition, harmonization, linkage, privacy protection, and computational environments. We outline computing infrastructure considerations relevant to rheumatology, including hybrid on-premises and cloud architectures. Sustained progress for AI applied to rheumatology will depend on deliberate investment in shared infrastructure, longitudinal data ecosystems, and governance models that balance innovation, privacy, reproducibility, and equitable clinical value.

متن کامل اصلی

متن در JumpToDate ذخیره نشده است.

برای بررسی دسترسی کتابخانه‌ای یا خرید، رکورد اصلی را باز کنید.

رفتن به منبع اصلی

کلیدواژه‌ها

Artificial intelligenceBig dataLarge language modelsMachine learningOntologies

در همین زیرشاخه

مقاله‌های مرتبط

PubMed2026

Improving Resident Knowledge of Artificial Intelligence Ethics and Prompting for Clinical Use.

BACKGROUND: The rapid introduction of AI into clinical practice shifts how we must teach resident trainees so they may become ethical patient-facing clinicians in an AI-integrated healthcare system. Currently, few published innovations assess outcomes beyond learner attitudes. We developed a pilot curricular innovation to equip postgraduate Internal Medicine resident trainees with the attitudes and knowledge needed to responsibly integ…

PubMed2026

Influence of artificial intelligence on xenotransplantation and regenerative medicine on the path toward ending the organ shortage.

The purpose of this review is to summarize the most influential and conceptually significant publications from the past 2 years, including substantial 2026 publications, and to identify emerging directions likely to shape xenotransplantation and regenerative medicine in the near future. Advances in artificial intelligence (AI) now support more structured anticipation of future developments by integrating patterns across experimental, c…

PubMed2026

Evaluating the quality, understandability, actionability, and readability of ChatGPT-generated patient information on overactive bladder: A cross-sectional study.

This cross-sectional study aimed to evaluate the quality, understandability, actionability, and readability of patient-oriented information generated by ChatGPT regarding overactive bladder (OAB). A total of 32 frequently asked patient questions related to OAB were categorized into 6 domains, including general information, diagnosis, lifestyle and behavioral management, medical treatment, minimally invasive treatments, and surgical tre…

PubMed2026

Metabolic signatures of triple-negative breast cancer.

Triple-negative breast cancer (TNBC), being one of the most aggressive subtypes of breast malignancies, is characterized by poor prognosis and limited treatment options. As a leading cause of cancer-related mortality among women, TNBC poses unique clinical challenges due to the absence of effective targeted therapies. Conventional treatment strategies, including chemotherapy, often suffer from significant drawbacks such as drug resista…