Accurate prognostic prediction remains a critical unmet need in advanced hepatocellular carcinoma (HCC). While machine learning (ML) models have demonstrated value in outcome prediction, the ability of large language models (LLMs) to perform structur... read more
Accurate identification of optimism and pessimism has important applications for individual health and societal well-being. Despite the growing interest in sentiment analysis and emotion detection, limited research has focused on the detection of opt... read more
Existing methods of grading atelectasis are typically subjective and not scalable. We aimed to develop an automated, deep learning-based framework to quantify and grade postoperative atelectasis. We retrospectively included all patients who underwent... read more
Synthetic data generation across domains can bridge gaps between visual training, skill development, and personalized surgical planning, ultimately transforming how surgeons and artificial intelligence (AI) systems prepare for the complexities of the... read more
Chronic pain is the hall-mark symptom of osteoarthritis (OA) and although several therapies are available, a sizeable number of patients do not gain adequate pain relief from these therapies. Predicting those patients who will not respond to current ... read more
Antibody folding and aggregation are major challenges in the development of relevant reagents and therapeutics. Antibodies face a biophysical trade-off; the immense diversity in complementarity-determining regions (CDRs), which is crucial for broad a... read more
Medical artificial intelligence (AI) has advanced rapidly, yet a comprehensive quantitative overview of its clinical evaluation landscape remains lacking. We conducted a scoping review of 218 systematic reviews published between September 2023 and Se... read more
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