Tag: clinical decision support
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Developing a Clinical Prediction Model for Ultra-Early Mild Acute Ischemic Stroke
Introduction to Ultra-Early Prediction in Mild AIS Cerebrovascular disease, particularly acute ischemic stroke (AIS) caused by cerebral atherosclerosis, remains a leading cause of disability and mortality in China. Early identification of patients at risk of progression or poor outcomes is crucial to guiding therapeutic decisions and improving functional recovery. Recent advances focus on ultra-early prediction…
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Anthropic Expands Claude for Healthcare to Transform Medical AI
Anthropic Raises the Stakes in Healthcare AI Anthropic is accelerating its push into regulated healthcare with a major expansion of its AI offerings. The company introduced Claude for Healthcare, a version of its Claude family tuned for medical contexts, data privacy, and compliance demands. As large language models (LLMs) become more integrated into high-stakes domains,…
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Video-Based Machine Learning Models Predicting Deep Brain Stimulation Outcomes in Parkinson’s Disease
Overview Deep brain stimulation (DBS) has transformed the treatment landscape for Parkinson’s disease (PD), offering symptom relief for many patients who do not respond adequately to medication. As DBS becomes more widely available, clinicians face the challenge of selecting the right candidates and predicting who will benefit most. Recent advances in video-based machine learning (ML)…
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Establishment and Validation of a Prognostic Nomogram Model for Hepatocellular Carcinoma: A Comprehensive Study
Introduction Liver cancer remains a global health challenge, with hepatocellular carcinoma (HCC) representing the majority of cases. In China, HCC is a leading cause of cancer-related mortality, contributing to a substantial portion of the worldwide burden each year. This study focuses on constructing and validating a robust prognostic nomogram to predict overall survival (OS) in…
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AI Diagnostics in Physical Therapy: Attitudes, Barriers, and Clinical Implications
Introduction: AI in a hands-on field Artificial intelligence (AI) is transforming many corners of healthcare, yet physical therapy has been slower to adopt AI-powered diagnostics compared with some medical specialties. Physical therapists (PTs) traditionally rely on hands-on assessments, patient history, and nuanced clinical reasoning to determine diagnoses, prognoses, and treatment plans. As AI tools mature—from…
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Attitudes of Physical Therapists Toward AI Diagnostics: Barriers, Enablers, and Clinical Implications
Overview: AI Diagnostics and Physical Therapy Artificial intelligence (AI) is reshaping many medical disciplines, yet physical therapy has unique considerations. Unlike fields that rely heavily on imaging or laboratory data, physical therapy emphasizes hands-on assessments, functional outcomes, and individualized patient engagement. As AI-powered diagnostic and decision-support tools begin to surface in musculoskeletal care, physical therapists…
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Developing and Validating a Machine Learning Model to Predict Heart Failure Risk in Chinese Adults with Type 2 Diabetes
Overview Type 2 diabetes mellitus (T2DM) remains a major public health challenge in China, affecting a substantial portion of the adult population and acting as an independent risk factor for heart failure (HF). Recent work in this field focuses on leveraging machine learning (ML) to improve risk stratification and guide early interventions. This article summarizes…
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A Simple Memory Tool for Early Recognition of Rare Lymphoma That Shows on the Skin
Groundbreaking Tool Aims to Accelerate Diagnosis Researchers from Trinity, in collaboration with UK partners, have developed a simple yet powerful diagnostic aid designed to help clinicians recognize a rare type of lymphoma that manifests on the skin. By supporting frontline clinicians with an easy-to-remember tool, the project seeks to shorten the time to diagnosis and…
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Endoscopic Intervention in Renal Failure with Upper Gastrointestinal Bleeding: Efficacy and Predictive Modeling
Introduction Upper gastrointestinal bleeding (UGIB) represents a critical challenge in patients with renal insufficiency. Renal impairment complicates hemostasis, alters pharmacokinetics of medications, and can influence the success rates of endoscopic interventions. This article reviews the efficacy of endoscopy in this high‑risk population, identifies disease severity thresholds where intervention may fail to reduce mortality, and describes…
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Pan-Cancer Prognosis AI Model Boosts Accuracy Across Cancers
Introduction: A New Era in Pan-Cancer Prognosis Recent advances in artificial intelligence are reshaping how clinicians predict cancer outcomes. A multimodal AI model named MICE (Multimodal data Integration via Collaborative Experts) has demonstrated notable improvements in pan-cancer prognosis prediction. By integrating pathology images, genomics, and clinical data, MICE shows strong generalizability across 30 cancer types,…