Processing unstructured clinical texts is often necessary to support certain tasks in biomedicine, such as matching patients to clinical trials. Among other methods, domain-specific language models have been built to ...
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BACKGROUND COVID-19 accelerated the adaptation of telemedicine in surgical practice. Audio only telemedicine utilization is rare; however, this may be more accessible than video for many patients. There is limited dat...
BACKGROUND COVID-19 accelerated the adaptation of telemedicine in surgical practice. Audio only telemedicine utilization is rare; however, this may be more accessible than video for many patients. There is limited data examining the feasibility of this technology in the preoperative setting. METHODS We conducted a single institution review of patients evaluated for thoracic surgery oncologic indications from 2018 – 2022. Patients were stratified into phone and office based assessment on the type of their first preoperative visit. Primary outcomes were rate of day of surgery (DOS) cancelation and postoperative length of stay (LOS). RESULTS Overall, 741 patients met the inclusion criteria, including 374 in person and 367 via telemedicine (Table 1). The distribution of patients residing in the same county as our institution did not change based on type of preoperative visit (15% v. 17%, p=0.550). Additionally, the rate of DOS cancellation did not differ between in office and phone based groups (2% [7/374] and 3% [11/367], p=0.910). Patients in the telemedicine group did have a higher number of preoperative visits compared to the in office group (1.0 visits v. 1.4 visits, p<0.001). There was no difference in postoperative length of stay (p=0.600). CONCLUSIONS This single institution study of patients undergoing thoracic surgery for malignancy demonstrates that patients can feasibly undergo an audio only preoperative visit without increasing the rate of DOS cancellation or length of stay. This technology should remain an option for surgical patients going forward.
Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical manag...
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A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. With a mortality rate of 5.5 million per year, it ranks as the second leading cause of d...
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In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. The dataset contains inf...
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Aims This study assessed sex-specific proteomic profiles by cardiovascular disease (CVD) phenotype (coronary artery disease [CAD] vs coronary microvascular dysfunction [CMD]) and describe their role in sex-specific pa...
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We present the results of the WASSA 2023 Shared-Task 2: Emotion Classification on codemixed text messages (Roman Urdu + English), which included two tracks for emotion classification: multi-label and multi-class. The ...
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In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the...
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The pace of artificial intelligence (AI) integration into healthcare has accelerated with rapid advances in generative AI (GenAI). Gastroenterology and hepatology in particular will be transformed due to the multimoda...
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The pace of artificial intelligence (AI) integration into healthcare has accelerated with rapid advances in generative AI (GenAI). Gastroenterology and hepatology in particular will be transformed due to the multimodal workflows that integrate endoscopic video, radiologic imaging, tabular data, and unstructured note text. GenAI will impact the entire spectrum of clinical experience, from administrative tasks, diagnostic guidance and treatment recommendations. Unlike traditional machine learning approaches, GenAI is more flexible, with one platform able to be used across multiple tasks. Initial evidence suggests benefit in lower-level administrative tasks such as clinical documentation, medical billing and scheduling, and information tasks such as patient education and summarization of the medical literature. No evidence exists for GenAI solutions for more complex tasks relevant to clinical care, such as clinical reasoning for diagnostic and treatment decisions that may affect patient outcomes. Challenges of output reliability, data privacy, and useful integration remain, with potential solutions include robust validation, regulatory oversight, and "human-AI teaming" strategies to ensure safe, effective deployment. We remain optimistic in the potential of GenAI to augment clinical expertise due to the adaptability of GenAI to handle multiple data modalities to obtain and focus relevant information flows and the human-friendly interfaces that facilitate ease of use. We believe that the potential of GenAI for dynamic human-algorithmic interactions may allow for a degree of clinician-directed customization to enhance human presence.
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