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Neurosurgery emphasizes the criticality of accurate differential diagnoses, with diagnostic delays posing significant health and economic challenges. As large language models (LLMs) emerge as transformative tools in healthcare, this study seeks to elucidate their role in assisting neurosurgeons with the differential diagnosis process, especially during preliminary consultations. This study employed 3 chat-based LLMs, ChatGPT (versions 3.5 and 4.0), Perplexity AI, and Bard AI, to evaluate their diagnostic accuracy. Each LLM was prompted using clinical vignettes, and their responses were recorded to generate differential diagnoses for 20 common and uncommon neurosurgical disorders. Disease-specific prompts were crafted using Dynamed, a clinical reference tool. The accuracy of the LLMs was determined based on their ability to identify the target disease within their top differential diagnoses correctly. For the initial differential, ChatGPT 3.5 achieved an accuracy of 52.63%, while ChatGPT 4.0 performed slightly better at 53.68%. Perplexity AI and Bard AI demonstrated 40.00% and 29.47% accuracy, respectively. As the number of considered differentials increased from 2 to 5, ChatGPT 3.5 reached its peak accuracy of 77.89% for the top 5 differentials. Bard AI and Perplexity AI had varied performances, with Bard AI improving in the top 5 differentials at 62.11%. On a disease-specific note, the LLMs excelled in diagnosing conditions like epilepsy and cervical spine stenosis but faced challenges with more complex diseases such as Moyamoya disease and amyotrophic lateral sclerosis. LLMs showcase the potential to enhance diagnostic accuracy and decrease the incidence of missed diagnoses in neurosurgery. [ABSTRACT FROM AUTHOR]

In this work, a selective solar absorber made of nanostructured titanium gratings deposited on an ultrathin MgF 2 spacer and a tungsten ground film is proposed and experimentally demonstrated. Normal absorptance of the fabricated solar absorber is characterized to be higher than 0.9 in the UV, visible and, near infrared (IR) regime, while the mid-IR emittance is around 0.2. The high broadband absorption in the solar spectrum is realized by the excitation of surface plasmon and magnetic polariton resonances, while the low mid-IR emittance is due to the highly reflective nature of the metallic components. Further directional and polarized reflectance measurements show wide-angle and polarization-insensitive high absorption within solar spectrum. Temperature-dependent spectroscopic characterization indicates that the optical properties barely change at elevated temperatures up to 350 °C. The solar-to-heat conversion efficiency with the fabricated metamaterial solar absorber is predicted to be 78% at 100 °C without optical concentration or 80% at 400 °C with 25 suns. The performance could be further improved with better fabrication processes and geometric optimization during metamaterial design. The strong spectral selectivity, favorable diffuse-like behavior, and good thermal stability make the metamaterial selective absorber promising for significantly enhancing solar thermal energy harvesting in various systems at mid to high temperatures. [ABSTRACT FROM AUTHOR]