Publications by authors named "O Badri"

In oral and maxillofacial surgery, the evolution of minimally invasive techniques has revolutionised the management of temporomandibular joint (TMJ) disorders. At the forefront of this advancement lies TMJ arthroscopy, a procedure that offers a precise approach to diagnosing and treating joint issues. TMJ arthroscopy is indicated in various clinical scenarios where a detailed evaluation and targeted interventions within the TMJ are required to alleviate symptoms, improve joint function, and enhance the overall quality of life of patients suffering from TMJ disorders.

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Article Synopsis
  • Advances in deep learning systems show promise for improving clinical decision-making in medical diagnoses, but the effectiveness of combining physician expertise with machine learning is still uncertain, particularly when dealing with underrepresented populations.
  • A large-scale study involving nearly 850 physicians evaluated their diagnostic accuracy using a teledermatology simulation with 364 skin disease images, revealing that specialist dermatologists had an accuracy of 38% while primary-care physicians had only 19%.
  • Although the integration of fair deep learning assistance improved overall diagnostic accuracy by over 33%, it highlighted and worsened the existing diagnostic disparities between skin tones, showing that enhancing accuracy doesn't eliminate bias in the system.
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Background: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race.

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