AIMC Topic: Natural Language Processing
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CoD-MIL: Chain-of-Diagnosis Prompting Multiple Instance Learning for Whole Slide Image Classification.
IEEE transactions on medical imaging
Mar 17, 2025
Multiple instance learning (MIL) has emerged as a prominent paradigm for processing the whole slide image with pyramid structure and giga-pixel size in digital pathology. However, existing attention-based MIL methods are primarily trained on the imag...
Automated Identification of Stroke Thrombolysis Contraindications from Synthetic Clinical Notes: A Proof-of-Concept Study.
Cerebrovascular diseases extra
Mar 17, 2025
INTRODUCTION: Timely thrombolytic therapy improves outcomes in acute ischemic stroke. Manual chart review to screen for thrombolysis contraindications may be time-consuming and prone to errors. We developed and tested a large language model (LLM)-bas...
Current Applications and Developments of Natural Language Processing in Kidney Transplantation: A Scoping Review.
Transplantation proceedings
Mar 16, 2025
BACKGROUND AND OBJECTIVE: Natural language processing (NLP) is a subfield of artificial intelligence that enables computers to process human language. As most human interactions today involve the internet and electronic devices, NLP tools quickly bec...
How to leverage large language models for automatic ICD coding.
Computers in biology and medicine
Mar 14, 2025
ICD coding, which involves assigning appropriate ICD codes to clinical notes, is essential for healthcare tasks such as health expense claims, insurance claims, and disease research. Manual ICD coding is time-consuming and prone to errors, increasing...
[Focus: artificial intelligence in medicine-Legal aspects of using large language models in clinical practice].
Innere Medizin (Heidelberg, Germany)
Mar 14, 2025
BACKGROUND: The use of artificial intelligence (AI) and natural language processing (NLP) methods in medicine, particularly large language models (LLMs), offers opportunities to advance the healthcare system and patient care in Germany. LLMs have rec...
Evaluating AI-generated patient education materials for spinal surgeries: Comparative analysis of readability and DISCERN quality across ChatGPT and deepseek models.
International journal of medical informatics
Mar 13, 2025
BACKGROUND: Access to patient-centered health information is essential for informed decision-making. However, online medical resources vary in quality and often fail to accommodate differing degrees of health literacy. This issue is particularly evid...
Multi-domain Urdu fake news detection using pre-trained ensemble model.
Scientific reports
Mar 13, 2025
Fake News (FN) dissemination on websites and online platforms influences human behaviours, socio-political domains, and the sovereignty of a country. The outpour of biased news and propaganda on online portals can be addressed by restricting online p...
Using Generative AI to Extract Structured Information from Free Text Pathology Reports.
Journal of medical systems
Mar 13, 2025
Manually converting unstructured text pathology reports into structured pathology reports is very time-consuming and prone to errors. This study demonstrates the transformative potential of generative AI in automating the analysis of free-text pathol...
Compliance Evaluation with ChatGPT for Diagnosis and Treatment in Patients Brought to the ED with a Preliminary Diagnosis of Stroke.
Prehospital emergency care
Mar 13, 2025
OBJECTIVES: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatGPT has gained attention in the field of health care, particularly for its potential applications in ...
Application of the LDA model to identify topics in telemedicine conversations on the X social network.
BMC health services research
Mar 12, 2025
The evolution experienced by global society, in the post-COVID 19 era, is marked by the quite obligatory use of digital media in many sectors, as is the case for the health sector. Quite frequently, both patients and health professionals use social m...
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