Publications by authors named "D Orringer"

The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development.

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Unlabelled: QUESTIONS AND RECOMMENDATIONS FROM THE PRIOR VERSION OF THESE GUIDELINES WITHOUT CHANGE: TARGET POPULATION: Adult patients (age ≥ 18 years) who have suspected low-grade diffuse glioma.

Question: What are the optimal neuropathological techniques to diagnose low-grade diffuse glioma in the adult?

Recommendation: Level I Histopathological analysis of a representative surgical sample of the lesion should be used to provide the diagnosis of low-grade diffuse glioma. Level III Both frozen section and cytopathologic/smear evaluation should be used to aid the intra-operative assessment of low-grade diffuse glioma diagnosis.

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Purpose: Balancing surgical margins and functional outcomes is crucial during radical prostatectomy for prostate cancer. Stimulated Raman histology (SRH) is a novel, real-time imaging technique that provides histologic images of fresh, unprocessed, and unstained tissue within minutes, which can be interpreted by either humans or artificial intelligence.

Materials And Methods: Twenty-two participants underwent robotic-assisted laparoscopic radical prostatectomy (RALP) with intraoperative SRH surgical bed assessment.

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Article Synopsis
  • Accurate intraoperative diagnosis of primary CNS lymphoma (PCNSL) is vital for surgical decisions but is challenging due to similar features with other CNS diseases; a new method combines stimulated Raman histology (SRH) with deep learning to improve this process.
  • The RapidLymphoma system uses a portable Raman microscope to create virtual images of tissue samples in under three minutes and employs a deep learning model trained on 54,000 images, allowing it to detect PCNSL and differentiate it from other conditions effectively.
  • In testing, RapidLymphoma achieved a high accuracy rate of 97.81%, performing better than traditional methods, and demonstrated its capability to identify specific histological features crucial for diagnosis, providing quick feedback
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A critical challenge in glioma treatment is detecting tumour infiltration during surgery to achieve safe maximal resection. Unfortunately, safely resectable residual tumour is found in the majority of patients with glioma after surgery, causing early recurrence and decreased survival. Here we present FastGlioma, a visual foundation model for fast (<10 s) and accurate detection of glioma infiltration in fresh, unprocessed surgical tissue.

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