Artificial intelligence (AI) offers a solution to glaucoma care inequities driven by uneven resource distribution, but its real-world implementation remains limited. Here, we introduce Multi-Glau, an three-tier AI system tailored to China’s hierarchical healthcare…
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Symptom monitoring using electronic patient-reported outcomes (ePROs) has demonstrated benefits for patients with cancer, yet the systematic effects for lung cancer remains unknown. This study performed a literature search in Medline, Embase, Cochrane library,…
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Low-income populations have disproportionately low completion of recommended healthcare services, from missed vaccinations to cancer screenings. While machine learning models help identify high-risk patients for targeted treatment, they have rarely been evaluated for quality…
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Subspecialty knowledge barriers have limited the adoption of large language models (LLMs) in oncology. We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung,…
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Electronic health records (EHR) contain data from disparate sources, spanning various biological and temporal scales. In this work, we introduce the Multiple Embedding Model for EHR (MEME), a deep learning framework for clinical decision…
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Fundus fluorescein angiography (FFA) is the gold standard for diagnosing chorioretinal diseases, but its interpretation requires significant expertise and time. Despite generative AI’s enormous potential in medical report generation, automatic FFA interpretation lacks robust…
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Immunohistochemistry (IHC) is crucial for the clinical categorisation of breast cancer cases. Deep generative models may offer a cost-effective alternative by virtually generating IHC images from hematoxylin and eosin samples. This review explores the…
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Large language models (LLMs) demonstrate significant potential in healthcare applications, but clinical deployment is limited by privacy concerns and insufficient medical domain training. This study investigated whether retrieval-augmented generation (RAG) can improve locally deployable…
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This study introduces a Bayesian multidimensional hierarchical item response theory (MHIRT) model to improve patient-reported outcome (PRO) assessments in total knee arthroplasty (TKA). Traditional unidimensional scoring fails to capture the multifaceted nature of recovery….
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Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is always…
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