3 research outputs found
Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective
Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology
New Strategies in Modeling Electronic Structures and Properties with Applications to Actinides
This chapter discusses contemporary quantum chemical methods and provides
general insights into modern electronic structure theory with a focus on
heavy-element-containing compounds. We first give a short overview of
relativistic Hamiltonians that are frequently applied to account for
relativistic effects. Then, we scrutinize various quantum chemistry methods
that approximate the -electron wave function. In this respect, we will
review the most popular single- and multi-reference approaches that have been
developed to model the multi-reference nature of heavy element compounds and
their ground- and excited-state electronic structures. Specifically, we
introduce various flavors of post-Hartree--Fock methods and optimization
schemes like the complete active space self-consistent field method, the
configuration interaction approach, the Fock-space coupled cluster model, the
pair-coupled cluster doubles ansatz, also known as the antisymmetric product of
1 reference orbital geminal, and the density matrix renormalization group
algorithm. Furthermore, we will illustrate how concepts of quantum information
theory provide us with a qualitative understanding of complex electronic
structures using the picture of interacting orbitals. While modern quantum
chemistry facilitates a quantitative description of atoms and molecules as well
as their properties, concepts of quantum information theory offer new
strategies for a qualitative interpretation that can shed new light onto the
chemistry of complex molecular compounds.Comment: 43 pages, 3 figures, Version of Recor