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"Research Letter"
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Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders
This study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic brain bank Clinico‐Pathological Conferences were analyzed. The LLMs provided multiple pathologic diagnoses and their rationales, which were compared with the final clinical diagnoses made by physicians. ChatGPT‐3.5, ChatGPT‐4, and Google Bard correctly made primary diagnoses in 32%, 52%, and 40% of cases, respectively, while correct diagnoses were included in 76%, 84%, and 76% of cases, respectively. These findings highlight the potential of artificial intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences. This study assessed the capability of large language models, namely ChatGPT and Google Bard, in predicting neuropathologic diagnoses from 25 cases presented at Mayo Clinic brain bank clinicopathological conferences. ChatGPT‐4 rendered correct diagnoses in 84% of cases, whereas ChatGPT‐3.5 and Google Bard each achieved 76%. These findings highlight the potential of large language models in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences.
Journal Article
Robocrystallographer: automated crystal structure text descriptions and analysis
Our ability to describe crystal structure features is of crucial importance when attempting to understand structure–property relationships in the solid state. In this paper, the authors introduce robocrystallographer, an open-source toolkit for analyzing crystal structures. This package combines new and existing open-source analysis tools to provide structural information, including the local coordination and polyhedral type, polyhedral connectivity, octahedral tilt angles, component-dimensionality, and molecule-within-crystal and fuzzy prototype identification. Using this information, robocrystallographer can generate text-based descriptions of crystal structures that resemble descriptions written by human crystallographers. The authors use robocrystallographer to investigate the dimensionalities of all compounds in the Materials Project database and highlight its potential in machine learning studies.
Journal Article
Guidelines in predicting phase formation of high-entropy alloys
With multiple elements mixed at equal or near-equal molar ratios, the emerging, high-entropy alloys (HEAs), also named multi-principal elements alloys (MEAs), have posed tremendous challenges to materials scientists and physicists, e.g., how to predict high-entropy phase formation and design alloys. In this paper, we propose some guidelines in predicting phase formation, using thermodynamic and topological parameters of the constituent elements. This guideline together with the existing ones will pave the way toward the composition design of MEAs and HEAs, as well as property optimization based on the composition–structure–property relationship.
Journal Article
On the identification of Sb2Se3 using Raman scattering
Robust evidences are presented showing that the Raman mode around 250 cm−1 in the Sb2Se3 thin films does not belong to this binary compound. The laser power density dependence of the Raman spectrum revealed the formation of Sb2O3 for high values of laser intensity power density excitation under normal atmospheric conditions. To complement this study, the Sb2Se3 films were characterized by x-ray diffraction during in situ annealing. Both these measurements showed that the Sb2Se3 compound can be replaced by Sb2O3. A heat-assisted chemical process explains these findings. Furthermore, Raman conditions required to perform precise measurements are described.
Journal Article
MET fusions and splicing variants is a strong adverse prognostic factor in astrocytoma, isocitrate dehydrogenase mutant
Liu et al. describe the adverse prognostic role of MET fusions and splicing variants in astrocytoma, isocitrate dehydrogenase mutant. On this basis, MET fusions and splicing variants was suggested to be a biomarker for the diagnosis of high‐grade astrocytoma, isocitrate dehydrogenase mutant.
Journal Article
Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses
There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.
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