AI Article Synopsis
- The study explores how artificial intelligence tools can assist clinicians in memory clinics and identifies the preferences and challenges faced by users like clinicians, patients, and care partners.
- Both clinician and patient groups showed a positive attitude towards using these tools for diagnosis and prognosis, highlighting factors like user-friendliness and improved diagnostic accuracy as motivators.
- Barriers include poor integration with electronic patient records and concerns about losing important clinical information, suggesting the need for improved tool design and implementation.
Article Abstract
Background: Computer tools based on artificial intelligence could aid clinicians in memory clinics in several ways, such as by supporting diagnostic decision-making, web-based cognitive testing, and the communication of diagnosis and prognosis.
Objective: This study aims to identify the preferences as well as the main barriers and facilitators related to using computer tools in memory clinics for all end users, that is, clinicians, patients, and care partners.
Methods: Between July and October 2020, we sent out invitations to a web-based survey to clinicians using the European Alzheimer's Disease Centers network and the Dutch Memory Clinic network, and 109 clinicians participated (mean age 45 years, SD 10; 53/109, 48.6% female). A second survey was created for patients and care partners. They were invited via Alzheimer Europe, Alzheimer's Society United Kingdom, Amsterdam Dementia Cohort, and Amsterdam Aging Cohort. A total of 50 patients with subjective cognitive decline, mild cognitive impairment, or dementia (mean age 73 years, SD 8; 17/34, 34% female) and 46 care partners (mean age 65 years, SD 12; 25/54, 54% female) participated in this survey.
Results: Most clinicians reported a willingness to use diagnostic (88/109, 80.7%) and prognostic (83/109, 76.1%) computer tools. User-friendliness (71/109, 65.1%); Likert scale mean 4.5, SD 0.7), and increasing diagnostic accuracy (76/109, 69.7%; mean 4.3, SD 0.7) were reported as the main factors stimulating the adoption of a tool. Tools should also save time and provide clear information on reliability and validity. Inadequate integration with electronic patient records (46/109, 42.2%; mean 3.8, SD 1.0) and fear of losing important clinical information (48/109, 44%; mean 3.7, SD 1.2) were most frequently indicated as barriers. Patients and care partners were equally positive about the use of computer tools by clinicians, both for diagnosis (69/96, 72%) and prognosis (73/96, 76%). In addition, most of them thought favorably regarding the possibility of using the tools themselves.
Conclusions: This study showed that computer tools in memory clinics are positively valued by most end users. For further development and implementation, it is essential to overcome the technical and practical barriers of a tool while paying utmost attention to its reliability and validity.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686488 | PMC |
| http://dx.doi.org/10.2196/31053 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Fully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python.
Skelet Muscle
December 2024
Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada.
Background: INTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.
Methods: We detail an open-source fully-automated iterative threshold-seeking algorithm (ITSA) for segmenting IMF from T1-weighted MRI of the calf and thigh within three cohorts (CaMos Hamilton (N = 54), AMBERS (N = 280), OAI (N = 105)) selecting adults 45-85 years of age.
EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance.
BMC Genomics
December 2024
School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Background: The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this.
View Article and Find Full Text PDFMultilingual SEIR public opinion propagation model with social enhancement mechanism and cross transmission mechanisms.
Sci Rep
December 2024
School of Statistics and Mathematics, Inner Mongolia University of Finance and Economics, Hohhot, 010070, China.
The propagation of public opinion in multilingual environments presents unique challenges due to the diversity of languages, cultures, and values. This study develops an SEIR-based model tailored for multilingual contexts, incorporating mechanisms such as social enhancement, forgetting, and cross-transmission. The model's purpose is to improve transparency, inclusivity, and effectiveness in public opinion management, particularly in diverse linguistic settings.
View Article and Find Full Text PDFEvaluation of long axial field-of-view (LAFOV) PET/CT for post-treatment dosimetry in Yttrium-90 radioembolization of liver tumors: a comparative study with conventional SPECT imaging.
Eur J Nucl Med Mol Imaging
December 2024
Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, Bern, 3010, Switzerland.
Purpose: Long axial field-of-view (LAFOV) positron emission tomography/computed tomography (PET/CT) scanners enable high sensitivity and wide anatomical coverage. Therefore, they seem ideal to perform post-selective internal radiation therapy (SIRT) Y scans, which are needed, to confirm that the dose is delivered to the tumors and that healthy organs are spared. However, it is unclear to what extent the use of LAFOV PET is feasible and which dosimetry approaches results in accurate measurements.
View Article and Find Full Text PDFExploring spiking neural networks for deep reinforcement learning in robotic tasks.
Sci Rep
December 2024
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", Università di Bologna, 40126, Bologna, Italy.
Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving as the medium for asynchronous communication among neurons. Due to their inherent ability to capture input dynamics, SNNs hold great promise for deep networks in Reinforcement Learning (RL) tasks.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!