Results 201 to 210 of about 73,459 (292)

Evaluating the Readability of AI-Generated Patient Information on Chronic Diseases. [PDF]

Chronic Dis Transl Med
Shafau F, Wahl C.
europepmc   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

Macromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig, Yannik Köster, Timo Koswig, Punith Raviswamy, Subhash V. S. Ganti, Stefan Zechel, Christopher Kuenneth, Ulrich S. Schubert   +7 more
wiley   +1 more source

Evaluating the Readability of Pediatric Neurocutaneous Syndromes-Related Patient Education Material Created by a Custom GPT With Retrieval Augmentation. [PDF]

JMIR Dermatol
Ede N, Okereke R.
europepmc   +1 more source

New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models

Materials Genome Engineering Advances, EarlyView.
This review first introduces the diversified applications of large language models in materials discovery. Subsequently, the evolution of autonomous experimentation platforms empowered by large language models is analyzed. Finally, four key future research interests are proposed to develop embodied large models for driving autonomous experimentation ...
Zhen Song, Yishuai Cai, Yadong Yu, Pan Xiang, Minglong Li, Zhe Liu, Mengyan Dai   +6 more
wiley   +1 more source

Revolutionizing patient care: assessing ai-created discharge education for total hip replacement patients. [PDF]

BMC Med Educ
Topan H, Baydoğan GM, Sürme Y.
europepmc   +1 more source

What Is Required for AI to Improve the Assessment and Treatment of Patients With Lower Urinary Tract Dysfunction? ICI‐RS 2025

Neurourology and Urodynamics, EarlyView.
ABSTRACT Introduction Artificial intelligence (AI) is poised to improve the diagnosis and management of lower urinary tract dysfunction (LUTD). Its effective deployment requires prioritization, regulatory oversight, rigorous validation, and clinician and patient engagement.
Glenn T. Werneburg, Michel Wyndaele, John E. Speich, Enrico Finazzi Agro, Mo Belal, Sachin Malde, George Bou Kheir, Emre Huri, Arjun K. Nambiar, Giovanni Mosiello, Riccardo Lombardo, Phil Toozs‐Hobson, Christopher R. Chapple, Alan J. Wein, Paul Abrams, Kevin Rademakers   +15 more
wiley   +1 more source

Utility of ChatGPT-4o in Creating Patient Handouts in Ophthalmology: A Comparison With American Academy of Ophthalmology Educational Materials. [PDF]

Transl Vis Sci Technol
Wu LL, Hong AT, Davuluru SS, Moreno-Diaz EA, Gutiérrez-Hernández JC, Martinez-Camarillo JC, Xu BY, Zhang-Nunes S.   +7 more
europepmc   +1 more source

Viscosity Reduction in Diluted Polyethylene Melts: A Comparative Study of Semiempirical, Viscoelastic, and Equation‐of‐State Modeling Frameworks

Polymer Engineering &Science, EarlyView.
Schematic of the in‐line rheometry setup and the evaluation of three distinct modeling frameworks (semiempirical, viscoelastic, and EOS) to predict viscosity reduction in diluted polyethylene melts via time‐concentration superposition (Illustration generated via ChatGPT).
Ernst Georg Viehböck, Alexander Hammer, Markus Kirchmayr, Christof Murnig, Christian Paulik, Gerald Berger‐Weber   +5 more
wiley   +1 more source

Comparative Evaluation of Artificial Intelligence Chatbots in Delivering Palliative Care Education to Intensive Care Unit Caregivers- A Cross-platform Analysis: A Brief Communication. [PDF]

Indian J Crit Care Med
Singh R, Gondode PG, Duggal S, Nayak SS.
europepmc   +1 more source

Are we misdiagnosing ensemble forecast reliability? On the insufficiency of spread–error and rank‐based reliability metrics

Quarterly Journal of the Royal Meteorological Society, EarlyView.
We demonstrate that the spread–error relationship, rank histogram, and continuous rank probability score reliability component can falsely indicate reliability under climatological variance biases, yielding ensemble members that are overly or insufficiently extreme.
Arlan Dirkson, Mark Buehner
wiley   +1 more source